bluuebunny/crossref_metadata_2025 huggingface dataset:
‘No sé’
Authors: González Montserrat
Published: July 2018

The aim of this paper1 is to analyze and discuss the semantic-pragmatic scope that epistemic phrases such as Spanish ‘no sé’ (I don’t know) play in oral opinion reports, a genre that implies a strong presence of value judgements and engagement on the part of the speaker.

The stance frame that such epistemic phrases provide as fixed epistemic formulas has been pointed out by several authors that work in the interactional epistemic domain (Kärkkäinen 2003, 2007; Scheibman 2000, 2001; Thompson 2002), exploring whether the scope of these formulas (I think, I don’t know, I guess, I thought and I remember) extends over something that has yet to be verbalized (forward scope) or over something that has already been verbalized in the preceding turn (backward scope). The working hypothesis is that, contrary to conversational genre, where such fixed formulas tend to provide a forward scope, in opinion reports the scope is twofold, with a stronger presence of backward scope, in the case of ‘no sé’. Findings suggest: (i) strong presence of the epistemic phrasal form (‘no sé’), rather than the predicative one, in the genre analyzed; (ii) predominant role of ‘no sé’ as attitude marker, to convey affect (to construe emotional responses), judgement (to convey moral evaluations) and appreciation (to construe the value of things).
Tenses in interaction
Authors: Rodríguez Rosique Susana
Published: July 2018

This chapter analyzes the discursive functions performed by the future in Spanish. In order to do so, it departs from a definition of the future based on the deictic ‘distance forward’ instruction, which may be projected along a subjectivity axle and cross different levels of meaning. When distance is projected upon the utterance, the future plays several discursive roles and traverses a variety of categories that go beyond evidentiality. More specifically, the future in Spanish can be a powerful tool to persuade and convince (persuasive future); it can also help control disagreement as part of a counter-argumentation strategy (concessive future); or it can participate in an assessment process (mirative future). Information occurring in the future must have been previously activated for these values to arise, which naturally happens in the context of interaction.
Positioning of self in interaction
Authors: Aijmer Karin
Published: July 2018

Look, listen, come on, excuse me are examples of imperatives of verbs which have developed into discourse markers with an attention-getting function. It is shown that the functional markers are used by adolescents to position themselves in relation to what is said and other participants in the conversation in order to mark their disalignment with the norms of adult society. In the adolescents’ world impoliteness is the norm associated with creating a positive self-image of the speakers. Attention-getters are also an important part of how adolescents perform as tellers of narratives or anecdotes. Adolescents use attention-getters for irony and ‘mock politeness’ and in joking competitive exchanges.
Occitan, verb second and the Medieval Romance word order debate
Authors: Wolfe Sam
Published: July 2018

This study presents a discussion of the word order properties of Old Occitan, a Medieval Romance language which remains under-studied in comparison to many of its sister languages. I argue that it was a V2 system and in particular that the locus of the V2 property was a low left-peripheral head, namely Fin, yielding a descriptively ‘relaxed’ V2 grammar, with systematic V-to-Fin0 movement but widespread verb-third or more orders. Data are presented from a wide range of related properties that show that Occitan occupies a unique position within the wider typology of V2 languages.
Reformulating prior speaker’s turn in Finnish
Authors: Sorjonen Marja-Leena
Published: July 2018

This chapter examines the Finnish particles siis,
eli(kkä) and nii(n) et(tä)
when they occupy the turn-initial position. The chapter focuses on
the use of these particles when they preface a single type of
action, a reformulation of the prior speaker’s turn or more extended
stretch of talk. In contrast to reformulations without a prefacing
particle, the particles are used for contextualizing the turn by
displaying their speaker’s stance vis-à-vis the action being done.
It will be shown that while the prefaces share a range of
characteristics, most importantly making an explicit link to the
prior talk and projecting a reformulation to come, each of them
displays a distinctive epistemic stance (e.g. degree of certainty;
basis of the reformulation). The chapter sets the turn-initial use
of the particles in relation to their other types of usages and
etymology.
Turn-initial naja in German
Authors: Golato Andrea
Published: July 2018

This paper investigates the German token naja in
turn initial position in everyday conversation. The paper makes the
argument that naja is produced in situations when
speakers are negotiating some form of break with prior utterances in
the interaction:1 Prior
research has indicated that in second-pair parts,
naja prefaces disagreements. The current paper
shows that naja is also used in initiating actions
that serve to return to a previous sequence that was not brought to
completion due to actions by the coparticipant(s) or outsiders of
the interaction. In addition, naja is used in third
position turns in which speakers are backing down from a previously
held position. In initiating actions and third positions,
naja serves to indicate the break and
simultaneously initiates actions to overcome it. The findings hold
both for cases in which subsequent talk is prosodically integrated
to naja and the cases in which
naja is not prosodically integrated to the
subsequent talk.
‘Life finds a way’: mapping a post-positivist marxian science
Authors: Kingsmith A. T. , von Bargen Julian
Published: July 2018

The rise of populist ethnonationalistmovements – and the breakdown of global liberal cosmopolitanism – caught most political analysts and theorists by surprise. This article takes this failure of political expertise as a challenge to reconsider radical Left approaches in making sense of political turmoil, economic crises, and class. Specifically, this article examines two key Marxian concepts, class and crisis, in relation to chaos. The authors argue that political expertise fails because the infinite velocities of chaos cannot be indefinitely contained categorically for three reasons: (1) because chaos understood as unpredictability is a property inherent to reality; (2) because there is no Archimedean point from outside of being in the world to objectively observe the world; and (3) because observing the world is always linked to intervening in it, a process through which the observer and the observed are both changed. To this end, the authors seek to augment crisis and class as concepts with a capacity to capture the nonlinear ways in which existing class struggle has developed across space and over time. To capture these complexities, the authors develop dadascience, a scientific apparatus of constant abandonment designed to account for determinations in relation to indeterminacy.
Who’s afraid of the people? The debate between political agoraphobia and political agoraphilia
Authors: Dupuis-Déri Francis
Published: July 2018

Do the people have the political capacity to rule themselves, or do they need to be ruled by an elite for ‘the common good’? In order to clarify this question, I introduce two concepts: political agoraphobia and political agoraphilia. I begin by briefly explaining the tension between two attitudes toward the people: love and fear. I then systematically set out the arguments and counter-arguments on both sides of the controversy. The debate seems to end up in a rhetorical deadlock, since both the people and the elite may be seen as irrational, vulnerable to demagoguery, and factious. I therefore suggest that what is mainly at stake is not to demonstrate whether the people are to be trusted or not with regard to their political capacities. What is at stake, rather, is the recognition that this debate subsumes a fundamental political struggle between the desire for domination and the desire for egalitarian freedom, as well as the willingness to acknowledge the philosophical significance of democratic and popular political experiences.
An accurate and rapid continuous wavelet dynamic time warping algorithm for end-to-end mapping in ultra-long nanopore sequencing
Authors: Han Renmin , Li Yu , Gao Xin , Wang Sheng
Published: July 2018

Motivation

Long-reads, point-of-care and polymerase chain reaction-free are the promises brought by nanopore sequencing. Among various steps in nanopore data analysis, the end-to-end mapping between the raw electrical current signal sequence and the reference expected signal sequence serves as the key building block to signal labeling, and the following signal visualization, variant identification and methylation detection. One of the classic algorithms to solve the signal mapping problem is the dynamic time warping (DTW). However, the ultra-long nanopore sequencing and an order of magnitude difference in the sampling speed complexify the scenario and make the classical DTW infeasible to solve the problem.

Results

Here, we propose a novel multi-level DTW algorithm, continuous wavelet DTW (cwDTW), based on continuous wavelet transforms with different scales of the two signal sequences. Our algorithm starts from low-resolution wavelet transforms of the two sequences, such that the transformed sequences are short and have similar sampling rates. Then the peaks and nadirs of the transformed sequences are extracted to form feature sequences with similar lengths, which can be easily mapped by the original DTW. Our algorithm then recursively projects the warping path from a lower-resolution level to a higher-resolution one by building a context-dependent boundary and enabling a constrained search for the warping path in the latter. Comprehensive experiments on two real nanopore datasets on human and on Pandoraea pnomenusa demonstrate the efficiency and effectiveness of the proposed algorithm. In particular, cwDTW can gain remarkable acceleration with tiny loss of the alignment accuracy. On the real nanopore datasets, cwDTW can finish an alignment task in few seconds, which is about 3000 times faster than the original DTW. By successfully applying cwDTW on the tasks of signal labeling and ultra-long sequence comparison, we further demonstrate the power and applicability of cwDTW.

Availability and implementation

Our program is available at https://github.com/realbigws/cwDTW.

Supplementary information

Supplementary data are available at Bioinformatics online.
Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes
Authors: Alshahrani Mona , Hoehndorf Robert
Published: July 2018

Motivation

In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease’s (or patient’s) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse.

Results

We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprised of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network.

Availability and implementation

https://github.com/bio-ontology-research-group/SmuDGE
Predicting protein–protein interactions through sequence-based deep learning
Authors: Hashemifar Somaye , Neyshabur Behnam , Khan Aly A , Xu Jinbo
Published: July 2018

Motivation

High-throughput experimental techniques have produced a large amount of protein–protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data.

Results

We present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone. Our model efficiently applies a deep, Siamese-like convolutional neural network combined with random projection and data augmentation to predict PPIs, leveraging existing high-quality experimental PPI data and evolutionary information of a protein pair under prediction. Our experimental results show that DPPI outperforms the state-of-the-art methods on several benchmarks in terms of area under precision-recall curve (auPR), and computationally is more efficient. We also show that DPPI is able to predict homodimeric interactions where other methods fail to work accurately, and the effectiveness of DPPI in specific applications such as predicting cytokine-receptor binding affinities.

Availability and implementation

Predicting protein-protein interactions through sequence-based deep learning): https://github.com/hashemifar/DPPI/.

Supplementary information

Supplementary data are available at Bioinformatics online.
Topology independent structural matching discovers novel templates for protein interfaces
Authors: Mirabello Claudio , Wallner Björn
Published: July 2018

Motivation

Protein–protein interactions (PPI) are essential for the function of the cellular machinery. The rapid growth of protein–protein complexes with known 3D structures offers a unique opportunity to study PPI to gain crucial insights into protein function and the causes of many diseases. In particular, it would be extremely useful to compare interaction surfaces of monomers, as this would enable the pinpointing of potential interaction surfaces based solely on the monomer structure, without the need to predict the complete complex structure. While there are many structural alignment algorithms for individual proteins, very few have been developed for protein interfaces, and none that can align only the interface residues to other interfaces or surfaces of interacting monomer subunits in a topology independent (non-sequential) manner.

Results

We present InterComp, a method for topology and sequence-order independent structural comparisons. The method is general and can be applied to various structural comparison applications. By representing residues as independent points in space rather than as a sequence of residues, InterComp can be applied to a wide range of problems including interface–surface comparisons and interface–interface comparisons. We demonstrate a use-case by applying InterComp to find similar protein interfaces on the surface of proteins. We show that InterComp pinpoints the correct interface for almost half of the targets (283 of 586) when considering the top 10 hits, and for 24% of the top 1, even when no templates can be found with regular sequence-order dependent structural alignment methods.

Availability and implementation

The source code and the datasets are available at: http://wallnerlab.org/InterComp.

Supplementary information

Supplementary data are available at Bioinformatics online.
Branch-recombinant Gaussian processes for analysis of perturbations in biological time series
Authors: Penfold Christopher A , Sybirna Anastasiya , Reid John E , Huang Yun , Wernisch Lorenz , Ghahramani Zoubin , Grant Murray , Surani M Azim
Published: July 2018

Motivation

A common class of behaviour encountered in the biological sciences involves branching and recombination. During branching, a statistical process bifurcates resulting in two or more potentially correlated processes that may undergo further branching; the contrary is true during recombination, where two or more statistical processes converge. A key objective is to identify the time of this bifurcation (branch or recombination time) from time series measurements, e.g. by comparing a control time series with perturbed time series. Gaussian processes (GPs) represent an ideal framework for such analysis, allowing for nonlinear regression that includes a rigorous treatment of uncertainty. Currently, however, GP models only exist for two-branch systems. Here, we highlight how arbitrarily complex branching processes can be built using the correct composition of covariance functions within a GP framework, thus outlining a general framework for the treatment of branching and recombination in the form of branch-recombinant Gaussian processes (B-RGPs).

Results

We first benchmark the performance of B-RGPs compared to a variety of existing regression approaches, and demonstrate robustness to model misspecification. B-RGPs are then used to investigate the branching patterns of Arabidopsis thaliana gene expression following inoculation with the hemibotrophic bacteria, Pseudomonas syringae DC3000, and a disarmed mutant strain, hrpA. By grouping genes according to the number of branches, we could naturally separate out genes involved in basal immune response from those subverted by the virulent strain, and show enrichment for targets of pathogen protein effectors. Finally, we identify two early branching genes WRKY11 and WRKY17, and show that genes that branched at similar times to WRKY11/17 were enriched for W-box binding motifs, and overrepresented for genes differentially expressed in WRKY11/17 knockouts, suggesting that branch time could be used for identifying direct and indirect binding targets of key transcription factors.

Availability and implementation

https://github.com/cap76/BranchingGPs

Supplementary information

Supplementary data are available at Bioinformatics online.
Evaluating bacterial and functional diversity of human gut microbiota by complementary metagenomics and metatranscriptomics
Authors: Ranjan Ravi , Rani Asha , Finn Patricia W. , Perkins David L.
Published: July 2018

It is well accepted that dysbiosis of microbiota is associated with disease; however, the biological mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data to complement the interpretation of metagenomics (bacterial abundance). The purpose of the study was twofold, first to evaluate the bacterial diversity and differential gene expression of gut microbiota using complementary shotgun metagenomics (MG) and metatranscriptomics (MT) from same fecal sample. Second, to compare sequence data using different Illumina platforms and with different sequencing parameters as new sequencers are introduced and determine if the data are comparable on different platforms. In this study, we perform ultra-deep metatranscriptomic shotgun sequencing for a sample that we previously analyzed with metagenomics shotgun sequencing. We validated the sequencing and analysis methods using different Illumina platform, and with different sequencing and analysis parameters. Our results suggest that use of different Illumina platform did not lead to detectable bias in the sequencing data. The analysis of the sample using MG and MT approach shows that some species genes are more highly represented in the MT than in the MG, indicating that some species are highly metabolically active. Our analysis also shows that ~52% of the genes in the metagenome are in the metatranscriptome, and therefore are robustly expressed. The functions of the low and rare abundance bacterial species remain poorly understood. Our observations indicate that among the low abundant species analyzed in this study some were found to be more metabolically active compared to others and can contribute distinct profiles of biological functions that may modulate the host-microbiota and bacteria-bacteria interactions.
Transcriptome-wide association supplements genome-wide association in Zea mays
Authors: Kremling Karl A. G. , Diepenbrock Christine H. , Gore Michael A. , Buckler Edward S. , Bandillo Nonoy B.
Published: July 2018

Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The potential of using endophenotypes for dissecting traits of interest remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299 genotype and 7 tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation for agronomic and seed quality (carotenoid, tocochromanol) traits is regulatory. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits, beating the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This improves not only the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.

Author summary

We examined the ability to associate variability in gene expression directly with terminal phenotypes of interest, as a supplement linking genotype to phenotype. We found that transcriptome-wide association studies (TWAS) are a useful accessory to genome-wide association studies (GWAS). In a combined test with GWAS results, TWAS improves the capacity to re-detect genes known to underlie quantitative trait loci for kernel and agronomic phenotypes. This improves not only the capacity to link genes to phenotypes, but also illustrates the widespread importance of regulation for phenotype.
Predicting Growth and Carcass Traits in Swine Using Metagenomic Data and Machine Learning Algorithms
Authors: Maltecca Christian , Lu Duc , Schillebeeckx Costantino , McNulty Nathan P , Schwab Clint , Schull Caleb , Tiezzi Francesco
Published: July 2018

In this paper, we evaluated the power of metagenome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of metagenomic information taken at week 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back-fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting metagenomic information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of microbiome on feed efficiency as well as carcass and meat quality should be investigated.
Phosphorylation of LXRα impacts atherosclerosis regression by modulating monocyte/macrophage trafficking
Authors: Shrestha Elina , Voisin Maud , Barrett Tessa J. , Nishi Hitoo , Cantor David J. , Hussein Maryem A. , David Gregory , Pineda-Torra Inés , Fisher Edward A. , Garabedian Michael J.
Published: July 2018

LXRα activation in macrophages enhances regression of atherosclerotic plaques in mice by regulating genes crucial for cholesterol efflux, cell motility and inflammation. Diabetes, however, impairs plaque regression in mice. LXRα is phosphorylated at serine 198 (pS198), which affects the expression of genes controlling inflammation, lipid metabolism and cell movement. We hypothesize that LXRα function is affected by hyperglycemia through changes in LXRα pS198. Indeed, macrophages cultured in diabetes relevant high glucose versus normal glucose display alterations in LXR-dependent gene expression and increased LXRα pS198. We therefore examined the consequence of disrupting LXRα phosphorylation (S196A in mouse LXRα) during regression of atherosclerosis in normal and diabetic mice. We find that phosphorylation deficient LXRα S196A reduces macrophage retention in plaques in diabetes, which is predicted to be anti-atherogenic and enhance plaque regression. However, this favorable effect on regression is masked by increased monocyte infiltration in the plaque attributed to leukocytosis in LXRα S196A mice. RNA-seq of plaque macrophages from diabetic S196A mice shows increased expression of chemotaxis and decreased expression of cell adhesion genes, consistent with reduced macrophage retention by LXRα S196A. Thus, the non-phosphorylated form of LXRα precludes macrophage retention in the plaque. Our study provides the first evidence for a physiological role of LXRα phosphorylation in modulating atherosclerosis regression. Compounds that prevent LXRα phosphorylation or ligands that induce the conformation of non-phosphorylated LXRα may selectively enhance macrophage emigration from atherosclerotic plaques.
Combining citizen science and deep learning to amplify expertise in neuroimaging
Authors: Keshavan Anisha , Yeatman Jason D. , Rokem Ariel
Published: July 2018

Research in many fields has become increasingly reliant on large and complex datasets. “Big Data” holds untold promise to rapidly advance science by tackling new questions that cannot be answered with smaller datasets. While powerful, research with Big Data poses unique challenges, as many standard lab protocols rely on experts examining each one of the samples. This is not feasible for large-scale datasets because manual approaches are time-consuming and hence difficult to scale. Meanwhile, automated approaches lack the accuracy of examination by highly trained scientists and this may introduce major errors, sources of noise, and unforeseen biases into these large and complex datasets. Our proposed solution is to 1) start with a small, expertly labelled dataset, 2) amplify labels through web-based tools that engage citizen scientists, and 3) train machine learning on amplified labels to emulate expert decision making. As a proof of concept, we developed a system to quality control a large dataset of three-dimensional magnetic resonance images (MRI) of human brains. An initial dataset of 200 brain images labeled by experts were amplified by citizen scientists to label 722 brains, with over 80,000 ratings done through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on a combination of the citizen scientist labels that accounts for differences in the quality of classification by different citizen scientists. In an ROC analysis (on left out test data), the deep learning network performed as well as a state-of-the-art, specialized algorithm (MRIQC) for quality control of T1-weighted images, each with an area under the curve of 0.99. Finally, as a specific practical application of the method, we explore how brain image quality relates to the replicability of a well established relationship between brain volume and age over development. Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in emerging disciplines where specialized, automated tools do not already exist.
Generating Realistic Morphologies of Neurons in Rodent Hippocampus with DCGAN
Authors: Hadjiabadi Darian H.
Published: July 2018

Dendritic size and branching patterns are important features of neural form and function. However, current computational models of neuronal networks use simplistic cylindrical geometries to mimic dendritic arborizations. One reason for this is that current methods to generate dendritic trees have rigid a priori constraints. To address this, a deep convolutional generative adversarial network (DCGAN) trained on images of rodent hippocampal granule and pyramidal dendritic trees. Image features learned by the network were used to generate realistic dendritic morphologies. Results show that DCGANs achieved greater stability∗ and high generalization, as quantified by kernel maximum mean discrepancy, when exposed to instance noise and/or label smoothing during training. Trained models successfully generated realistic morphologies for both neuron types, with high false positive rate reported by expert reviewers. Collectively, DCGANs offer a unique opportunity to advance the geometry of neural modeling, and, therefore, to propel our understanding of neuronal function.

∗ A “stable/stabilized DCGAN”, as mentioned throughout this work, is a DCGAN which was stable throughout training.
Is the predation risk of mate-searching different between the sexes?
Authors: Torsekar Viraj R. , Isvaran Kavita , Balakrishnan Rohini
Published: July 2018

In animals that communicate for pair formation, generally one sex invests more effort in mate searching. Differential predation risk of mate searching between the sexes is hypothesised to determine which sex invests more effort in mate searching. Although searching by males is prevalent in most animals, in orthopteran insects and some other taxa females physically move to localise signalling males who are predominantly sedentary. Although the two sexes thus share mate searching effort in orthopterans, their behavioural strategies are different and sexual selection theory predicts that signalling males may be following the riskier strategy and incurring higher costs. However, relative levels of risk posed by the two mate searching strategies remain largely unexplored. Hence, we estimated the relative predation risk experienced in natural populations by signalling males and responding females. We did this by quantifying predation risk as a probability of mortality in the context of acoustic communication in a tree cricket,Oecanthus henryifrom its ecologically relevant predator, a lynx spider,Peucetia viridans. Spiders may perceive calling in males and movement in females by their ability to detect both airborne acoustic cues and substrate-borne vibratory cues. Probability of mortality was quantified by partitioning it into three spatial components at which crickets and spiders interact, using a combination of extensive field observations and manipulative experiments in a semi-natural setup. We found no differences in predation risk faced by calling males and responding females, supporting the prediction that similar sex-specific costs can explain shared mate searching responsibilities. Our findings therefore suggest that direct benefits offered by males to females upon pair formation may better explain shared mate searching effort between the sexes in orthopterans.
Bacterial dynamin-like protein DynA mediates lipid and content mixing and shows phospholipid specificity
Authors: Guo Lijun , Bramkamp Marc
Published: July 2018

The dynamins family of GTPases is involved in key cellular processes in eukaryotes, including vesicle trafficking and organelle division. The GTP hydrolysis cycle of dynamin translates to a conformational change in the protein structure, which forces the underlying lipid layer into an energetically unstable conformation that promotes membrane rearrangements. Many bacterial genomes encode dynamin-like proteins, but the biological function of these proteins has remained largely enigmatic. In recent years, our group has reported that the dynamin-like protein DynA fromBacillus subtilismediates nucleotide-independent membrane tethering in vitro and contributes to the innate immunity of bacteria against membrane stress and phage infection. However, so far the mechanism of membrane stress response and the role of GTP hydrolysis remain unclear. Here, we employed content mixing and lipid mixing assays in reconstituted systems to study if the dynamin-like protein DynA fromB. subtilisinduces membrane full fusion, and further test the possibility that GTP hydrolysis of DynA may act on the fusion-through-hemifusion pathway. Our results based on fluorescence resonance energy transfer (FRET) indicated that DynA could induce aqueous content mixing even in absence of GTP. Moreover, DynA-induced membrane fusion in vitro is a thermo-promoted slow response. Surprisingly, digestion of protein mediated an instantl rise of content exchange, supporting the assumption that disassembly of DynA is the fundamental power for fusion-through-hemifusion.
The effect of stride length on lower extremity joint kinetics at various gait speeds
Authors: McGrath Robert L. , Ziegler Melissa L. , Pires-Fernandes Margaret , Knarr Brian A. , Higginson Jill S. , Sergi Fabrizio
Published: July 2018

Robot-assisted training is a promising tool under development for improving walking function based on repetitive goal-oriented task practice. The challenges in developing the controllers for gait training devices that promote desired changes in gait is complicated by the limited understanding of the human response to robotic input. A possible method of controller formulation can be based on the principle of bio-inspiration, where a robot is controlled to apply the change in joint moment applied by human subjects when they achieve a gait feature of interest. However, it is currently unclear how lower extremity joint moments are modulated by even basic gaitspatio-temporal parameters.

In this study, we investigated how sagittal plane joint moments are affected by a factorial modulation of two important gait parameters: gait speed and stride length. We present the findings obtained from 20 healthy control subjects walking at various treadmill-imposed speeds and instructed to modulate stride length utilizing real-time visual feedback. Implementing a continuum analysis of inverse-dynamics derived joint moment profiles, we extracted the effects of gait speed and stride length on joint moment throughout the gait cycle. Moreover, we utilized a torque pulse approximation analysis to determine the timing and amplitude of torque pulses that approximate the difference in joint moment profiles between stride length conditions, at all gait speed conditions.

Our results show that gait speed has a significant effect on the moment profiles in all joints considered, while stride length has more localized effects, with the main effect observed on the knee moment during stance, and smaller effects observed for the hip joint moment during swing and ankle moment during the loading response. Moreover, our study demonstrated that trailing limb angle, a parameter of interest in programs targeting propulsion at push-off, was significantly correlated with stride length. As such, our study has generated assistance strategies based on pulses of torque suitable for implementation via a wearable exoskeleton with the objective of modulating stride length, and other correlated variables such as trailing limb angle.
Both rare and common genetic variants contribute to autism in the Faroe Islands
Authors: Leblond Claire S , Cliquet Freddy , Carton Coralie , Huguet Guillaume , Mathieu Alexandre , Kergrohen Thomas , Buratti Julien , Lemière Nathalie , Cuisset Laurence , Bienvenu Thierry , Boland Anne , Deleuze Jean-François ,   consortium GenMed, Stora Tormodur , Biskupstoe Rannva , Halling Jónrit , Andorsdóttir Guðrið , Billstedt Eva , Gillberg Christopher , Bourgeron Thomas
Published: July 2018

The number of genes associated with autism is increasing, but few studies have been performed on epidemiological cohorts and in isolated populations. Here, we investigated 357 individuals from the Faroe Islands including 36 individuals with autism, 136 of their relatives and 185 non-autism controls. Data from SNP array and whole exome sequencing revealed that individuals with autism compared to controls had a higher burden of copy-number variants (p< 0.05), higher inbreeding status (p< 0.005) and higher load of homozygous deleterious variants (p< 0.01). Our analysis supports the role of several genes/loci associated with autism (e.g.NRXN1, ADNP, 22q11 deletion) and identified new truncating (e.g. GRIK2, ROBO1, NINLandIMMP2L) or recessive deleterious variants (e.g. KIRELL3andCNTNAP2) affecting autism-risk genes. It also revealed three genes involved in synaptic plasticity,RIMS4, KALRNand PLA2G4A, carryingde novodeleterious variants in individuals with autism without intellectual disability. In summary, our analysis provides a better understanding of the genetic architecture of autism in isolated populations by highlighting the role of both common and rare gene variants and pointing at new autism-risk genes. It also indicates that more knowledge about how multiple genetic hits affect neuronal function will be necessary to fully understand the genetic architecture of autism.
Statistical shape analysis of large datasets based on diffeomorphic iterative centroids
Authors: Cury Claire , Glaunès Joan A. , Toro Roberto , Chupin Marie , Shumann Gunter , Frouin Vincent , Poline Jean-Baptiste , Colliot Olivier ,   and the Consortium Imagen
Published: July 2018

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1000 surfaces, and we are able to analyse this dataset in 26 hours using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus in healthy subjects.
Evolution of Endothelin signaling and diversification of adult pigment pattern inDaniofishes
Authors: Spiewak Jessica E. , Bain Emily J. , Liu Jin , Kou Kellie , Sturiale Samantha L. , Patterson Larissa B. , Diba Parham , Eisen Judith S. , Braasch Ingo , Ganz Julia , Parichy David M.
Published: July 2018

Fishes of the genusDanioexhibit diverse pigment patterns that serve as useful models for understanding the genes and cell behaviors underlying the evolution of adult form. Among these species, zebrafishD. rerioexhibit several dark stripes of melanophores with sparse iridophores that alternate with light interstripes of dense iridophores and xanthophores. By contrast, the closely related speciesD. nigrofasciatushas an attenuated pattern with fewer melanophores, stripes and interstripes. Here we demonstrate species differences in iridophore development that presage the fully formed patterns. Using genetic and transgenic approaches we identify the secreted peptide Endothelin-3 (Edn3)—a known melanogenic factor of tetrapods—as contributing to reduced iridophore proliferation and fewer stripes and interstripes inD. nigrofasciatus. We further show the locus encoding this factor is expressed at lower levels inD. nigrofasciatusowing tocis-regulatory differences between species. Finally, we show that functions of two paralogous loci encoding Edn3 have been partitioned between skin and non-skin iridophores. Our findings reveal genetic and cellular mechanisms contributing to pattern differences between these species and suggest a model for evolutionary changes in Edn3 requirements across vertebrates.

Author Summary

Neural crest derived pigment cells generate the spectacular variation in skin pigment patterns among vertebrates. Mammals and birds have just a single skin pigment cell, the melanocyte, whereas ectothermic vertebrates have several pigment cells including melanophores, iridophores and xanthophores, that together organize into a diverse array of patterns. In the teleost zebrafish,Danio rerio, an adult pattern of stripes depends on interactions between pigment cell classes and between pigment cells and their tissue environment. The close relative,D. nigrofasciatushas fewer stripes and prior analyses suggested a difference between these species that lies extrinsic to the pigment cells themselves. A candidate for mediating this difference is Endothelin-3 (Edn3), essential for melanocyte development in warm-blooded animals, and required by all three classes of pigment cells in an amphibian. We show that Edn3 specifically promotes iridophore development inDanio, and that differences in Edn3 expression contribute to differences in iridophore complements, and striping, betweenD. rerioandD. nigrofasciatus. Our study reveals a novel function for Edn3 and provides new insights into how changes in gene expression yield morphogenetic outcomes to effect diversification of adult form.
polyDFEv2.0: Testing for invariance of the distribution of fitness effects within and across species
Authors: Tataru Paula , Bataillon Thomas
Published: July 2018

Distributions of fitness effects (DFE) of mutations can be inferred from site frequency spectrum (SFS) data. There is mounting interest to determine whether distinct genomic regions and/or species share a common DFE, or whether evidence exists for differences among them. polyDFEv2.0 fits multiple SFS datasets at once and provides likelihood ratio tests for DFE invariance across datasets. Simulations show that testing for DFE invariance across genomic regions within a species requires models accounting for heterogeneous genealogical histories underlying SFS data in these regions. Not accounting for these heterogeneities will result in the spurious detection of DFE differences.
Integrated soybean transcriptomics, metabolomics, and chemical genomics reveal the importance of the phenylpropanoid pathway and antifungal activity in resistance to the broad host range pathogenSclerotinia sclerotiorum
Authors: Ranjan Ashish , Westrick Nathaniel M. , Jain Sachin , Piotrowski Jeff S. , Ranjan Manish , Kessens Ryan , Stiegman Logan , Grau Craig R. , Smith Damon L. , Kabbage Mehdi
Published: July 2018

Sclerotinia sclerotiorum, a predominately necrotrophic fungal pathogen with a broad host range, causes a significant yield limiting disease of soybean called Sclerotinia stem rot (SSR). Resistance mechanisms against SSR are poorly understood, thus hindering the commercial deployment of SSR resistant varieties. We used a multiomic approach utilizing RNA-sequencing, Gas chromatography-mass spectrometry-based metabolomics and chemical genomics in yeast to decipher the molecular mechanisms governing resistance toS.sclerotiorumin soybean. Transcripts and metabolites of two soybean recombinant inbred lines, one resistant, and one susceptible toS.sclerotiorumwere analyzed in a time course experiment. The combined results show that resistance toS.sclerotiorumin soybean is associated in part with an early accumulation of JA-Ile ((+)-7-iso-Jasmonoyl-L-isoleucine), a bioactive jasmonate, increased ability to scavenge reactive oxygen species (ROS), and importantly, a reprogramming of the phenylpropanoid pathway leading to increased antifungal activities. Indeed, we noted that phenylpropanoid pathway intermediates such as, 4-hydroxybenzoate, ferulic acid and caffeic acid were highly accumulated in the resistant line.In vitroassays show that these metabolites and total stem extracts from the resistant line clearly affectS.sclerotiorumgrowth and development. Using chemical genomics in yeast, we further show that this antifungal activity targets ergosterol biosynthesis in the fungus, by disrupting enzymes involved in lipid and sterol biosynthesis. Overall, our results are consistent with a model where resistance toS.sclerotiorumin soybean coincides with an early recognition of the pathogen, leading to the modulation of the redox capacity of the host and the production of antifungal metabolites.

Author Summary

Resistance to plant fungal pathogens with predominately necrotrophic lifestyles is poorly understood. In this study, we useSclerotinia sclerotiorumand soybean as a model system to identify key resistance components in this crop plant. We employed a variety of omics approaches in combination with functional studies to identify plant processes associated with resistance toS.sclerotiorum. Our results suggest that resistance to this pathogen is associated in part with an earlier induction of jasmonate signaling, increased ability to scavenge reactive oxygen species, and importantly, a reprogramming of the phenylpropanoid pathway resulting in increased antifungal activities. These findings provide specific plant targets that can exploited to confer resistance toS.sclerotiorumand potentially other pathogens with similar lifestyle.