bluuebunny/crossref_metadata_2025_split huggingface dataset:
Engineering recurrent neural networks from task-relevant manifolds and dynamics
Authors: Pollock Eli , Jazayeri Mehrdad 
Published: December 2019

AbstractMany cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons.Author SummaryNeurons in the brain form intricate networks that can produce a vast array of activity patterns. To support goal-directed behavior, the brain must adjust the connections between neurons so that network dynamics can perform desirable computations on behaviorally relevant variables. A fundamental goal in computational neuroscience is to provide an understanding of how network connectivity aligns the dynamics in the brain to the dynamics needed to track those variables. Here, we develop a mathematical framework for creating recurrent neural network models that can address this problem. Specifically, we derive a set of linear equations that constrain the connectivity to afford a direct mapping of task-relevant dynamics onto network activity. We demonstrate the utility of this technique by creating and analyzing a set of network models that can perform a simple working memory task. We then extend the approach to show how additional constraints can furnish networks whose dynamics are controlled flexibly by external inputs. Finally, we exploit the flexibility of this technique to explore the robustness and capacity limitations of recurrent networks. This network synthesis method provides a powerful means for generating and validating hypotheses about how task-relevant computations can emerge from network dynamics.
Evolutionary genomics of structural variation in Asian rice (Oryza sativa) and its wild progenitor (O. rufipogon)
Authors: Kou Yixuan , Liao Yi , Toivainen Tuomas , Lv Yuanda , Tian Xinmin , Emerson J.J , Gaut Brandon S. , Zhou Yongfeng 
Published: December 2019

ABSTRACTStructural variants (SVs) are a largely unstudied feature of plant genome evolution, despite the fact that SVs contribute substantially to phenotypes. In this study, we discovered structural variants (SVs) across a population sample of 358 high-coverage, resequenced genomes of Asian rice (Oryza sativa) and its wild ancestor (O. rufipogon). In addition to this short-read dataset, we also inferred SVs from whole-genome assemblies and long-read data. Comparisons among datasets revealed different features of genome variability. For example, genome alignment identified a large (~4.3 Mb) inversion in indica rice varieties relative to an outgroup, and long-read analyses suggest that ~9% of genes from this outgroup are hemizygous. We focused, however, on the resequencing sample to investigate the population genomics of SVs. Clustering analyses with SVs recapitulated the rice cultivar groups that were also inferred from SNPs. However, the site-frequency spectrum of each SV type -- which included inversions, duplications, deletions, translocations and mobile element insertions -- was skewed toward lower frequency variants than synonymous SNPs, suggesting that SVs are predominantly deleterious. The strength of these deleterious effects varied among SV types, with inversions especially deleterious, and across transposable element (TE) families. Among TEs SINE andmarinerinsertions were especially deleterious, due to stronger selection against their insertions. We also used SVs to study domestication by contrasting between rice andO. rufipogon. Cultivated genomes contained ~25% more derived SVs thanO. rufipogon, suggesting these deleterious SVs contribute to the cost of domestication. We also used SVs to study the effects of positive selection on the rice genome. Generally, the search for domestication genes were enriched for known candidates, suggesting some utility for SVs towards this purpose. More importantly, we detected hundreds to thousands of genes gained and lost during domestication, many of which are predicted to contribute to traits of agronomic interest.
Investigating mechanisms of polarized light sensitivity in the small white butterflyPieris rapae
Authors: Blake Adam J. , Hahn Gina S. , Grey Hayley , Kwok Shelby , McIntosh Deby , Gries Gerhard 
Published: December 2019

AbstractThere is an ever increasing number of arthropod taxa shown to have polarization sensitivity throughout their compound eyes. However, the mechanisms underlying arthropod perception of polarized reflections from objects such as plants are not well understood. The small white butterfly,Pieris rapae, has been demonstrated to exploit foliar polarized reflections, specifically the degree of linear polarization (DoLP), to recognize host plants. The well-described visual system ofP. rapaeincludes several photoreceptor types (red, green, blue) that are sensitive to polarized light. Yet, the mechanism underlying the behavioral responses ofP. rapaeto stimuli with differentDoLPsremains unknown. To investigate potential mechanisms, we designed several two-choice behavioral bioassays, displaying plant images on paired LCD monitors which allowed for independent control of polarization, color and intensity. We found that shifts in image intensity had a similar effect onP. rapaepreferences for stimuli dissimilar inDoLPand dissimilar in color, suggestingDoLPdifferences are perceived as color. When aDoLPchoice was offered between plant images manipulated in a manner to minimizing the response of blue, red, or blue and red photoreceptors,P. rapaeshifted its preference forDoLP, suggesting a role for red, green and blue polarization-sensitive photoreceptors. Modeling ofP. rapaephotoreceptor responses to test stimuli suggests that differentialDoLPis not perceived solely as a color difference. Our combined results suggest thatP. rapaefemales process and interpret polarization reflections in a way different from that described for other polarization-sensitive taxa.
A mathematical model exhibiting the effect of DNA methylation on the stability boundary in cell-fate networks
Authors: Chen Tianchi , Al-Radhawi M. Ali , Sontag Eduardo D. 
Published: December 2019

AbstractCell-fate networks are traditionally studied within the framework of gene regulatory networks. This paradigm considers only interactions of genes through expressed transcription factors and does not incorporate chromatin modification processes. This paper introduces a mathematical model that seamlessly combines gene regulatory networks and DNA methylation, with the goal of quantitatively characterizing the contribution of epigenetic regulation to gene silencing. The “Basin of Attraction percentage” is introduced as a metric to quantify gene silencing abilities. As a case study, a computational and theoretical analysis is carried out for a model of the pluripotent stem cell circuit as well as a simplified self-activating gene model. The results confirm that the methodology quantitatively captures the key role that methylation plays in enhancing the stability of the silenced gene state.
Protein Docking and Steered Molecular Dynamics Reveal Alternative Regulatory Sites on the SERCA Calcium Transporter
Authors: Alford Rebecca F. , Smolin Nikolai , Young Howard S. , Gray Jeffrey J. , Robia Seth L. 
Published: December 2019

AbstractThe transport activity of the calcium ATPase SERCA is modulated by an inhibitory interaction with a 52-residue transmembrane peptide, phospholamban (PLB). Biochemical and structural studies have revealed the primary inhibitory site on SERCA, but PLB has been hypothesized to interact with alternative sites on SERCA that are distinct from the inhibitory site. The present study was undertaken to test these hypotheses and explore structural determinants of SERCA regulation by PLB. Steered molecular dynamics (SMD) and membrane protein-protein docking experiments were performed to investigate the apparent affinity of PLB interactions with candidate sites on SERCA. We modeled the relative binding of PLB to several different conformations of SERCA, representing different enzymatic states sampled during the calcium transport catalytic cycle. Overall, the SMD and docking experiments suggest that the canonical binding site is preferred, but also provide evidence for alternative sites that are favorable for certain conformational states of SERCA.
Simple, Functional, Inexpensive Cell Extract forin vitroPrototyping of Proteins with Disulfide Bonds
Authors: Dopp Jared L. , Reuel Nigel F. 
Published: December 2019

AbstractIn vitroexpression of proteins fromE. coliextract is a useful method for prototyping and production of cytotoxic or unnatural products. However, proteins that have multiple disulfide bonds require custom extract that, to date, requires careful addition of exogenous isomerase enzymes or the use of expensive commercial kits. This cost and complexity currently limit access to some groups who wish to rapidly prototype proteins with disulfide bonds. Herein, we present a simple solution that does not require addition of supplemental enzymes. We use a commercially available SHuffle T7 ExpresslysYstrain ofE. colithat expresses both T7 RNAP and DsbC isomerase enzymes. We experimentally determine optimal growth conditions (IPTG induction and harvest times) to balance overall productivity and efficiency of disulfide bond formation using a luciferase (fromGaussia princeps) that contains five disulfide bonds as our reporter protein. We also demonstrate the ability for rapid prototyping by screening the activity of four luciferase candidates against ten luciferin analogues. To display the broad applicability of the extract, three other enzymes containing ≥3 disulfide bonds (hevamine, endochitinase A, and periplasmic AppA) were also expressed from minimal genetic templates that had undergone rolling circle amplification and confirmed via activity assays.
Data-driven algorithm for the diagnosis of behavioral variant frontotemporal dementia
Authors: Manera Ana L. , Dadar Mahsa , van Swieten John , Borroni Barbara , Sanchez-Valle Raquel , Moreno Fermin , LaForce Robert , Graff Caroline , Synofzik Matthis , Galimberti Daniela , Rowe James , Masellis Mario , Tartaglia Maria Carmela , Finger Elizabeth , Vandenberghe Rik , de Mendonça Alexandre , Tagliavini Fabrizio , Santana Isabel , Butler Chris , Gerhard Alex , Danek Adrian , Levin Johannes , Otto Markus , Frisoni Giovanni , Ghidoni Roberta , Sorbi Sandro , Rohrer Jonathan D , Ducharme Simon , Collins D. Louis ,   Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI),   GENetic Frontotemporal dementia Initiative (GENFI)
Published: December 2019

AbstractINTRODUCTIONBrain structural imaging is paramount for the diagnosis of behavioral variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis.METHODSA total of 515 subjects from two different bvFTD databases (training and validation cohorts) were included to perform voxel-wise deformation-based morphometry analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from morphometric differences in isolation and together with bedside cognitive scores.RESULTSAverage ten-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In a separate validation cohort of genetically confirmed bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added cognitive scores.DISCUSSIONThe random forest classifier developed can accurately predict bvFTD at the individual subject level.
Rewired signaling network in T cells expressing the chimeric antigen receptor (CAR)
Authors: Dong Rui , Libby Kendra A. , Blaeschke Franziska , Marson Alexander , Vale Ronald D. , Su Xiaolei 
Published: December 2019

AbstractThe chimeric antigen receptor (CAR) directs T cells to target and kill specific cancer cells. Despite the success of CAR T therapy in clinics, the intracellular signaling pathways that lead to CAR T cell activation remain unclear. Using CD19 CAR as a model, we report that, similar to the endogenous T cell receptor (TCR), antigen-engagement triggers the formation of CAR microclusters that transduce downstream signaling. However, CAR microclusters do not coalesce into a stable central supramolecular activation cluster (cSMAC). Moreover, LAT, an essential scaffold protein for TCR signaling, is not required for microcluster formation, immunological synapse formation, and actin remodeling following CAR activation. Meanwhile, CAR T cells still require LAT for the normal production of the cytokine IL-2. Together, these data show that CAR T cells can bypass LAT for a subset of downstream signaling outputs, thus revealing a rewired signaling pathway as compared to native T cells.
Microbial microdroplet culture system (MMC): an integrated platform for automated, high–throughput microbial cultivation and adaptive evolution
Authors: Jian Xingjin , Guo Xiaojie , Wang Jia , Tan Zheng Lin , Xing Xin-hui , Wang Liyan , Zhang Chong 
Published: December 2019

AbstractConventional microbial cell cultivation techniques are typically labor intensive, low throughput, and poor parallelization, rendering them inefficient. The development of automated, modular microbial cell micro–cultivation systems, particularly those employing droplet microfluidics, has gained attention for their high–throughput, parallel and highly efficient cultivation capabilities. Here, we report the development of a microbial microdroplet culture system (MMC), which is an integrated platform for automated, high–throughput cultivation and adaptive evolution of microorganisms. We demonstrated that the MMC yielded both accurate and reproducible results for the manipulation and detection of droplets. The superior performance of MMC for microbial cell cultivation was validated by comparing the growth curves of six microbial strains grown in MMC, conventional shake flasks or well plates. The highest incipient growth rate for all six microbial cell lines was achieved using MMC. We also conducted an 18–day process of adaptive evolution of a methanol–essentialEscherichia colistrain in MMC and obtained two strains exhibiting higher growth rates compared with the parent strain. Our study demonstrates the power of MMC to provide an efficient and reliable approach for automated, high–throughput microbial cultivation and adaptive evolution.
Categorical signaling of the strongest stimulus by an inhibitory midbrain nucleus
Authors: Schryver Hannah M. , Straka Malgorzata , Mysore Shreesh P. 
Published: December 2019

ABSTRACTThe isthmi pars magnocellularis (Imc), a group of inhibitory neurons in the vertebrate midbrain tegmentum, orchestrates stimulus competition and spatial selection in the optic tectum (OT). Here, we investigate the properties of relative-strength dependent competitive interactions within the barn owl Imc. Imc neurons exhibit switch-like as well as gradual response profiles as a function of relative stimulus strength, do so for competing stimuli both within and across sensory modalities, and signal the strongest stimulus in a dynamically flexible manner. Notably, Imc signals the strongest stimulus more categorically (with greater precision), and earlier than the OT. Paired recordings at spatially aligned Imc and OT sites reveal that although some properties of stimulus competition are correlated, others are set independently. Our results demonstrate that the Imc is itself an active site of competition, and may be the first site in the midbrain selection network at which stimulus competition is resolved.
Neural modelling of the semantic predictability gain under challenging listening conditions
Authors: Rysop Anna , Schmitt Lea-Maria , Obleser Jonas , Hartwigsen Gesa 
Published: December 2019

AbstractWhen speech intelligibility is reduced, listeners exploit constraints posed by semantic context to facilitate comprehension. The left angular gyrus (AG) has been argued to drive this semantic predictability gain. Taking a network perspective, we ask how the connectivity within language-specific and domain-general networks flexibly adapts to the predictability and intelligibility of speech. During continuous functional magnetic resonance imaging (fMRI), participants repeated sentences, which varied in semantic predictability of the final word and in acoustic intelligibility. At the neural level, highly predictable sentences led to stronger activation of left-hemispheric semantic regions including subregions of the AG (PGa, PGp) and posterior middle temporal gyrus when speech became more intelligible. The behavioural predictability gain of single participants mapped onto the same regions but was complemented by increased activity in frontal and medial regions. Effective connectivity from PGa to PGp increased for more intelligible sentences. In contrast, inhibitory influence from pre-supplementary motor area to left insula was strongest when predictability and intelligibility of sentences were either lowest or highest. This interactive effect was negatively correlated with behavioural predictability gain. Together, these results suggest that successful comprehension in noisy listening conditions relies on an interplay of semantic regions and concurrent inhibition of cognitive control regions when semantic cues are available.
Spike activity regulates vesicle filling at a glutamatergic synapse
Authors: Li Dainan , Zhu Yun , Huang Hai 
Published: December 2019

AbstractSynaptic vesicles need to be recycled and refilled rapidly to maintain high-frequency synaptic transmission. However, little is known about the control of transport of neurotransmitter into synaptic vesicles, which determines the contents of synaptic vesicles and the strength of synaptic transmission. Here we report that Na+substantially accumulated in the calyx of Held terminals of mouse during high-frequency spiking. The activity-induced elevation of cytosolic Na+activated vesicular Na+/H+exchanger, facilitated glutamate loading into synaptic vesicles and increased quantal size of asynchronous released vesicles, but did not affect the vesicle pool size or release probability. Consequently, presynaptic Na+increased the excitatory postsynaptic currents and was required to maintain the reliable high-frequency signal transmission from the presynaptic calyces to the postsynaptic MNTB neurons. Blocking Na+/H+activity with EIPA decreased the postsynaptic current and caused failures in postsynaptic firing. Therefore, during high-frequency synaptic transmission, when large amounts of glutamate are released, Na+accumulated in the terminals, activated vesicular Na+/H+exchanger, and regulated glutamate loading as a function of the level of vesicle release.Significant statementAuditory information is encoded by action potentials phase-locked to sound frequency at high rates. Large amount of synaptic vesicles are released during high-frequency synaptic transmission, accordingly, synaptic vesicles need to be recycled and refilled rapidly. We have recently found that a Na+/H+exchanger expressed on synaptic vesicles promotes vesicle filling with glutamate. Here we showed that during high-frequency signaling, when massive vesicles are released, Na+accumulates in terminals and facilitates glutamate uptake into synaptic vesicle. Na+thus accelerates vesicle replenishment and sustains reliable synaptic transmission.
A map of transcriptional heterogeneity and regulatory variation in human microglia
Authors: Young Adam MH , Kumasaka Natsuhiko , Calvert Fiona , Hammond Timothy R. , Knights Andrew , Panousis Nikolaos , Schwartzentruber Jeremy , Liu Jimmy , Kundu Kousik , Segel Michael , Murphy Natalia , McMurran Christopher E , Bulstrode Harry , Correia Jason , Budohoski Karol P , Joannides Alexis , Guilfoyle Mathew R , Trivedi Rikin , Kirollos Ramez , Morris Robert , Garnett Matthew R , Fernandes Helen , Timofeev Ivan , Jalloh Ibrahim , Holland Katherine , Mannion Richard , Mair Richard , Watts Colin , Price Stephen J , Kirkpatrick Peter J , Santarius Thomas , Soranzo Nicole , Stevens Beth , Hutchinson Peter J , Franklin Robin JM , Gaffney Daniel J 
Published: December 2019

AbstractMicroglia, the tissue resident macrophages of the CNS, are implicated in a broad range of neurological pathologies, from acute brain injury to dementia. Here, we profiled gene expression variation in primary human microglia isolated from 141 patients undergoing neurosurgery. Using single cell and bulk RNA sequencing, we defined distinct cellular populations of acutelyin vivo-activated microglia, and characterised a dramatic switch in microglial population composition in patients suffering from acute brain injury. We mapped expression quantitative trait loci (eQTLs) in human microglia and show that many disease-associated eQTLs in microglia replicate well in a human induced pluripotent stem cell (hIPSC) derived macrophage model system. Using ATAC-seq from 95 individuals in this hIPSC model we fine-map candidate causal variants at risk loci for Alzheimer’s disease, the most prevalent neurodegenerative condition in acute brain injury patients. Our study provides the first population-scale transcriptional map of a critically important cell for neurodegenerative disorders.