bluuebunny/crossref_metadata_2025_split huggingface dataset:
DKW basal salts improve micropropagation and callogenesis compared to MS basal salts in multiple commercial cultivars ofCannabis sativa
Authors: Page Serena R.G. , Monthony Adrian S. , Jones A. Maxwell P. 
Published: February 2020

AbstractMicropropagation ofCannabis sativais an emerging area for germplasm storage and large-scale production of clean plants. Existing protocols use a limited number of genotypes and are often not reproducible. Previous studies reported MS + 0.5 μM TDZ to be optimal forCannabisnodal micropropagation, yet our preliminary studies using nodal explants suggested this media may not be optimal. It resulted in excessive callus formation, hyperhydricity, low multiplication rates, and high mortality rates. Following an initial screen of four commonly used basal salt mixtures (MS, B5, BABI, and DKW), we determined that DKW produced the healthiest plants. In a second experiment, the multiplication rate and canopy area of explants grown on MS + 0.5 μM TDZ and DKW + 0.5 μM TDZ were compared using five drug-type cultivars to determine if the preference for DKW was genotype-dependent. Four cultivars had significantly higher multiplication rates on DKW + 0.5 μM TDZ with the combined average being 1.5x higher than explants grown on MS + 0.5 μM TDZ. The canopy area was also significantly larger on DKW + 0.5 μM TDZ for four cultivars with the combined average being twice as large as the explants grown on MS + 0.5 μM TDZ. In the third experiment, callogenesis was compared using a range of 2,4-D concentrations (0-30 μM) on both MS and DKW and similarly, callus growth was superior on DKW. This study presents the largest comparison of basal salt compositions on the micropropagation of five commercially grownCannabiscultivars to date.
Model-based analysis of polymorphisms in an enhancer reveals cis-regulatory mechanisms
Authors: Khajouei F , Samper N , Djabrayan NJ , Lunt B , Jiménez G , Sinha S 
Published: February 2020

ABSTRACTIt is challenging to predict the impact of small genetic changes such as single nucleotide polymorphisms on gene expression, since mechanisms involved in gene regulation and their cis-regulatory encoding are not well-understood. Recent studies have attempted to predict the functional impact of non-coding variants based on available knowledge of cis-regulatory encoding, e.g., transcription factor (TF) motifs. In this work, we explore the relationship between regulatory variants and cis-regulatory encoding from the opposite angle, using the former to inform the latter. We employ sequence-to-expression modeling to resolve ambiguities regarding gene regulatory mechanisms using information about effects of single nucleotide variations in an enhancer. We demonstrate our methodology using a well-studied enhancer of the developmental geneintermediate neuroblasts defective(ind) inD. melanogaster. We first trained the thermodynamics-based model GEMSTAT to relate the neuroectodermal expression pattern of ind to its enhancer’s sequence, and constructed an ensemble of models that represent different parameter settings consistent with available data for this gene. We then predicted the effects of every possible single nucleotide variation within this enhancer, and compared these to SNP data recorded in the Drosophila Genome Reference Panel. We chose specific SNPs for which different models in the ensemble made conflicting predictions, and tested their effect in vivo. These experiments narrowed in on one mechanistic model as capable of explaining the observed effects. We further confirmed the generalizability of this model to orthologous enhancers and other related developmental enhancers. In conclusion, mechanistic models of cis-regulatory function not only help make specific predictions of variant impact, they may also be learned more accurately using data on variants.STATEMENT OF SIGNIFICANCEA central issue in analyzing variations in the non-coding genome is to interpret their functional impact, and their connections to phenotype differences and disease etiology. Machine learning methods based on statistical modeling have been developed to associate genetic variants to expression changes. However, associations predicted by these models may not be functionally relevant, despite being statisticaly significant. We describe how mathematical modeling of gene expression can be employed to systematically study the non-coding sequence and its relationship to gene expression. We demonstrate our method in a well studied developmental enhancer of the fruitfly. We establish the efficacy of mathematical models in combination with the polymorphism data to reveal new mechanistic insights.
Exploring the selenium-over-sulfur substrate specificity and kinetics of a bacterial selenocysteine lyase
Authors: Johnstone Michael A. , Nelson Samantha J. , Van Groesbeck Christine , Self William T. 
Published: February 2020

ABSTRACTSelenium is a vital micronutrient in many organisms. While traces are required for utilization by the microbe, excess amounts are toxic; thus, selenium can be regarded as a biological “double-edged sword”. Selenium is chemically similar to the essential element sulfur, but curiously, evolution has selected the former over the latter for a subset of oxidoreductases. Enzymes involved in sulfur metabolism are less discriminate in terms of preventing selenium incorporation; however, its specific incorporation into selenoproteins reveals a highly discriminate process that is not completely understood. We have identified SclA, a selenocysteine lyase in the nosocomial pathogen,Enterococcus faecalis, and characterized its enzymatic activity and specificity for L-selenocysteine over L-cysteine. It is known that a single residue in the human selenocysteine lyase, D146, is considered to control selenocysteine specificity. Thus, using computational biology, we identified H100, a D146 ortholog in SclA, and generated mutant enzymes with site-directed mutagenesis. The proteins were overexpressed, purified, and characterized for their biochemical properties. All mutants exhibited varying levels of activity towards L-selenocysteine, suggesting a catalytic role for H100. Additionally, L-cysteine acted as a competitive inhibitor towards all enzymes with higher affinity than L-selenocysteine. Our findings offer key insight into the molecular mechanisms behind selenium-over-sulfur specificity and may further elucidate the role of selenocysteine lyasesin vivo.
Polymorphisms in human APOBEC3H differentially regulate ubiquitination and antiviral activity
Authors: Chesarino Nicholas M. , Emerman Michael 
Published: February 2020

AbstractThe APOBEC3 family of cytidine deaminases are an important part of the host innate immune defense against endogenous retroelements and retroviruses like human immunodeficiency virus (HIV).APOBEC3H(A3H) is the most polymorphic of the humanAPOBEC3genes, with four major haplotypes circulating in the population. Haplotype II is the only antivirally-active variant ofA3H, while the majority of the population possess independently destabilizing polymorphisms present in haplotype I (R105G) and haplotypes III and IV (N15del). Here, we show that instability introduced by either polymorphism is positively correlated with degradative ubiquitination, while haplotype II is protected from this modification. Inhibiting ubiquitination by mutating all of the A3H lysines increased expression of haplotypes III and IV, but these stabilized forms of haplotype III and IV had a strict nuclear localization, and did not incorporate into virions, nor exhibit antiviral activity, thus separating stabilization from function. On the other hand, the instability and functional deficiencies of haplotype III could be rescued by fusion to haplotype II, supporting a model by which antiviral A3H is actively stabilized through a cytoplasmic retention mechanism. Thus, the evolutionary loss of A3H activity in many humans involves functional deficiencies independent of protein stability.
An ERP index of real-time error correction within a noisy-channel framework of human communication
Authors: Ryskin Rachel , Stearns Laura , Bergen Leon , Eddy Marianna , Fedorenko Evelina , Gibson Edward 
Published: February 2020

AbstractRecent evidence suggests that language processing is well-adapted to noise in the input (e.g., speech errors, mishearing) and readily corrects the input via rational inference over possible intended sentences and probable noise corruptions. However, it remains unclear whether this inference takes the form of an offline re-analysis or a rapid, real-time correction to the representations of the input. We hypothesize that noise inferences happen online during processing and that well-studied ERP components may serve as a useful index of this process. In particular, a reduced N400 effect and increased P600 effect appear to accompany sentences where the probability that the message was corrupted by noise exceeds the probability that it was produced intentionally and perceived accurately. Indeed, semantic violations that are attributable to noise—for example, in “The storyteller could turn any incident into an amusingantidote”, where the implausible word “antidote” is orthographically and phonologically close to the intended “anecdote”—elicit a reduced N400 effect and larger P600 effect. Further, the magnitude of this P600 effect is shown to relate to the probability that the comprehender will retrieve a plausible alternative. This work thus adds to the growing body of literature that suggests that many aspects of language processing are well-adapted to noise in the input and opens the door to electrophysiologic investigations of these processes
Data-driven models reveal mutant cell behaviors important for myxobacterial aggregation
Authors: Zhang Zhaoyang , Cotter Christopher R. , Lyu Zhe , Shimkets Lawrence J. , Igoshin Oleg A. 
Published: February 2020

AbstractSingle mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology usingMyxococcus xanthusas a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants,csgAandpilC, that cannot aggregate unless mixed with wild type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust.
T-ALL Proliferation Relies on Glutamine Uptake and EAAT1-dependent Conversion of Glutamine to Aspartate and Nucleotides
Authors: Stanulović Vesna S. , Omair Shorog Al , Reed Michelle A.C. , Potluri Sandeep , Roberts Jennie , Perry Tracey A. , Sarkar Sovan , Pratt Guy , Günther Ulrich L. , Ludwig Christian , Hoogenkamp Maarten 
Published: February 2020

AbstractT-cell acute lymphoblastic leukaemia (T-ALL) is a cancer of the immune system. Approximately 20% of paediatric and 50% of adult T-ALL patients have refractory disease or relapse and die from the disease. To improve patient outcome new therapeutics are needed. With the aim to identify new therapeutic targets, we analysed the metabolic adaptations that T-ALL cells exhibit and found that glutamine uptake is essential for their proliferation. Isotope tracing experiments showed that glutamine fuels aspartate synthesis through the TCA cycle and that glutamine and glutamine-derived aspartate together supply three nitrogen atoms in purines and all but one atom in pyrimidine rings. We show that the glutamate-aspartate transporter EAAT1, which is normally only expressed in the CNS, is crucial for glutamine conversion to nucleotides and that T-ALL cell proliferation depends on EAAT1 function. Through this work, we identify EAAT1 as a novel therapeutic target for T-ALL treatment.
Recording the age of RNA with deamination
Authors: Rodriques Samuel G. , Chen Linlin M. , Liu Sophia , Zhong Ellen D. , Scherrer Joseph R. , Boyden Edward S. , Chen Fei 
Published: February 2020

AbstractTranscriptional programs implemented by cells often consist of complex temporal features, but current approaches to single-cell RNA sequencing only provide limited information about the dynamics of gene expression. Here, we present RNA timestamps, a method for inferring the age of individual RNAs in a sequencing-based readout by leveraging RNA editing. Timestamped RNAs include a RNA reporter motif that accumulates A to I edits over time, allowing the age of the RNA to be inferred with hour-scale accuracy. By combining observations of multiple timestamped RNAs driven by the same promoter, we are able to infer when in the past the promoter was active. We demonstrate that the system can infer the presence and timing of multiple past transcriptional events, with no prior knowledge. Finally, we apply this method to cluster single cells according to the times at which a particular transcriptional program was activated. RNA timestamps thus suggest a new approach for incorporating temporal information into RNA sequencing workflows.
ABCF1 regulates dsDNA-induced immune responses in human airway epithelial cells
Authors: Cao Quynh T. , Aguiar Jennifer A. , Tremblay Benjamin J-M , Abbas Nadin , Tiessen Nicholas , Revill Spencer , Makhdami Nima , Ayoub Anmar , Cox Gerard , Ask Kjetil , Doxey Andrew C. , Hirota Jeremy A. 
Published: February 2020

AbstractBackgroundThe airway epithelium represents a critical component of the human lung that helps orchestrate defences against respiratory tract viral infections, which are responsible for more than 2.5 million deaths/year globally. Innate immune activities of the airway epithelium rely Toll-like receptors (TLRs), nucleotide binding and leucine-rich-repeat pyrin domain containing (NLRP) receptors, and cytosolic nucleic acid sensors. ATP Binding Cassette (ABC) transporters are ubiquitous across all three domains of life – Archaea, Bacteria, and Eukarya – and expressed in the human airway epithelium. ABCF1, a unique ABC family member that lacks a transmembrane domain, has been defined as a cytosolic nucleic acid sensor that regulates CXCL10, interferon-β expression, and downstream type I interferon responses. We tested the hypothesis that ABCF1 functions as a dsDNA nucleic acid sensor in human airway epithelial cells important in regulating antiviral responses.MethodsExpression and localization experiments were performed using in situ hybridization and immunohistochemistry in human lung tissue from healthy subjects, while confirmatory transcript and protein expression was performed in human airway epithelial cells. Functional experiments were performed with siRNA methods in human airway epithelial cells. Complementary transcriptomic analyses were performed to explore the contributions of ABCF1 to gene expression patterns.ResultsUsing archived human lung and human airway epithelial cells, we confirm expression of ABCF1 gene and protein expression in these tissue samples, with a role for mediating CXCL10 production in response to dsDNA viral mimic challenge. Although, ABCF1 knockdown was associated with an attenuation of select genes involved in the antiviral responses, Gene Ontology analyses revealed a greater interaction of ABCF1 with TLR signaling suggesting a multifactorial role for ABCF1 in innate immunity in human airway epithelial cells.ConclusionABCF1 is a candidate cytosolic nucleic acid sensor and modulator of TLR signaling that is expressed at gene and protein levels in human airway epithelial cells. The precise level where ABCF1 protein functions to modulate immune responses to pathogens remains to be determined but is anticipated to involve IRF-3 and CXCL10 production.
A taxonomy of seizure dynamotypes
Authors: Saggio Maria Luisa , Crisp Dakota , Scott Jared , Karoly Phillippa J. , Kuhlmann Levin , Nakatani Mitsuyoshi , Murai Tomohiko , Dümpelmann Matthias , Schulze-Bonhage Andreas , Ikeda Akio , Cook Mark , Gliske Stephen V. , Lin Jack , Bernard Christophe , Jirsa Viktor , Stacey William 
Published: February 2020

ABSTRACTSeizures are a disruption of normal brain activity present across a vast range of species, diseases, and conditions. Here we introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory, and applied it to the analysis of EEG data. The “dynamotype” of a seizure is the part of its dynamic composition that defines its observable characteristics, including how it starts, evolves and terminates. Analyzing over 2000 focal-onset seizures recorded from 7 epilepsy centers on five continents, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain dynamotypes are more common), non-bijectivity (a patient may display multiple dynamotypes) and pairing preference (multiple dynamotypes may occur during one seizure). TSD not only provides a way to stratify patients in complement to present practical classifications but also guides biophysically based mechanistic approaches and provides a language to describe the most critical features of seizure dynamics.Impact statementTaxonomy of Seizure Dynamics (TSD) provides a rigorous method for classifying and quantifying seizures and a principled framework for understanding seizure initiation and propagation.
Neural correlates of newsvendor-based decision making in the human brain:An exploratory study to link neuroeconomics with neuroimaging using fNIRS
Authors: Wanniarachchi Hashini , Lang Yan , Wang Xinlong , Nerur Sridhar , Chen Kay-Yut , Liu Hanli 
Published: February 2020

AbstractNeuroeconomics with neuroimaging is a novel approach involving economics and neuroscience. The newsvendor problem (NP) is a prevalent economics concept that may be used to map brain activations during NP-evoked risky decision making. In this study, we hypothesized that key brain regions responsible for NP are dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). Twenty-seven human subjects participated in the study using 40 NP trials; the participants were randomly assigned to a group with a low-profit margin (LM) or high-profit margin (HM) treatment. Cerebral hemodynamic responses were recorded simultaneously during the NP experiments from all participants with a 77-channel functional Near-infrared Spectroscopy (fNIRS) system. After data preprocessing, general linear model was applied to generate brain activation maps, followed by statistical t-tests. The results showed that: (a) DLPFC and OFC were significantly evoked by NP versus baseline regardless of treatment types; (b) DLPFC and OFC were activated by HM versus baseline; and (c) DLPFC was activated during LM versus baseline. Furthermore, significant deactivation in right DLPFC was shown due to LM with respect to HM. This study affirms that DLPFC and OFC are two key cortical regions when solving NP. In particular, right DLPFC was found to be more deactivated under challenging risk decision making.
Decoding Attention Control and Selection in Visual Spatial Attention
Authors: Hong Xiangfei , Bo Ke , Meyyapan Sreenivasan , Tong Shanbao , Ding Mingzhou 
Published: February 2020

AbstractEvent-related potentials (ERPs) are used extensively to investigate the neural mechanisms of attention control and selection. The commonly applied univariate ERP approach, however, has left important questions inadequately answered. Here, we addressed two questions by applying multivariate pattern classification to multichannel ERPs in two spatial-cueing experiments (N= 56 in total): (1) impact of cueing strategies (instructional vs. probabilistic) and (2) neural and behavioral effects of individual differences. Following the cue onset, the decoding accuracy (cue left vs. cue right) began to rise above chance level earlier and remained higher in instructional cueing (∼80 ms) than in probabilistic cueing (∼160 ms), suggesting that unilateral attention focus leads to earlier and more distinct formation of the attentional set. A similar temporal sequence was also found for target-related processing (cued targets vs. uncued targets), suggesting earlier and stronger attention selection under instructional cueing. Across the two experiments, individuals with higher decoding accuracy during ∼460-660 ms post-cue showed higher magnitude of attentional modulation of target-evoked N1 amplitude, suggesting that better formation of anticipatory attentional state leads to better target processing. During target processing, individual difference in decoding accuracy was positively associated with behavioral performance (reaction time), suggesting that stronger selection of task-relevant information leads to better behavioral performance. Taken together, multichannel ERPs combined with machine learning decoding yields new insights into attention control and selection that are not possible with the univariate ERP approach, and along with the univariate ERP approach, provides a more comprehensive methodology to the study of visual spatial attention.
Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex
Authors: Almasi Ali , Meffin Hamish , Cloherty Shaun L. , Wong Yan , Yunzab Molis , Ibbotson Michael R. 
Published: February 2020

AbstractVisual object identification requires both selectivity for specific visual features that are important to the object’s identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognised as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we show that the balance between selectivity and invariance in complex cells is more diverse than thought. Phase invariance is frequently partial, thus retaining sensitivity to brightness polarity, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.