bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pan-phylum In Silico Analyses of Nematode Endocannabinoid Signalling Systems Highlight Novel Opportunities for Parasite Drug Target Discovery
Authors: Crooks Bethany A. , McKenzie Darrin , Cadd Luke C. , McCoy Ciaran J. , McVeigh Paul , Marks Nikki J. , Maule Aaron G. , Mousley Angela , Atkinson Louise E. 
Published: March 2022

ABSTRACTThe endocannabinoid signalling (ECS) system is a complex lipid signalling pathway that modulates diverse physiological processes in both vertebrate and invertebrate systems. In nematodes, knowledge of endocannabinoid (EC) biology is derived primarily from the free-living model species Caenorhabditis elegans, where ECS has been linked to key aspects of nematode biology. The conservation and complexity of nematode ECS beyond C. elegans is largely uncharacterised, undermining the understanding of ECS biology in nematodes including species with key importance to human, veterinary and plant health. In this study we exploited publicly available omics datasets, in silico bioinformatics and phylogenetic analyses to examine the presence, conservation and life-stage expression profiles of EC-effectors across phylum Nematoda. Our data demonstrate that: (i) ECS is broadly conserved across phylum Nematoda, including in therapeutically and agriculturally relevant species; (ii) EC-effectors appear to display clade and lifestyle-specific conservation patterns; (iii) filarial species possess a reduced EC-effector complement; (iv) there are key differences between nematode and vertebrate EC-effectors; (v) life stage-, tissue- and sex-specific EC-effector expression profiles suggest a role for ECS in therapeutically relevant parasitic nematodes. These data also highlight putative novel targets for anthelmintic therapies. To our knowledge, this study represents the most comprehensive characterisation of ECS pathways in phylum Nematoda and inform our understanding of nematode ECS complexity. Fundamental knowledge of nematode ECS systems will seed follow-on functional studies in key nematode parasites to underpin novel drug target discovery efforts.CONTRIBUTION TO THE FIELDThis manuscript reports the in silico characterisation of endocannabinoid (EC) signalling pathways across the nematode phylum. The physiological relevance and therapeutic potential of EC signalling in higher organisms has received significant attention. In contrast much of our knowledge on EC signalling in nematodes has been derived from the free-living nematode Caenorhabditis elegans where the EC signalling system appears to play key roles in nematode biology and features GPCRs distinct from vertebrate cannabinoid receptors. Unfortunately, the configuration and broader biological significance of EC signalling pathways across the nematode phylum, including in parasites of agricultural, veterinary and medical significance, remains unknown. The in silico exploration of the nematode EC signalling system reported here will provide baseline data on novel neuronal signalling pathways to seed future drug target discovery pipelines for parasites.
Enhancing bacteriophage therapeutics throughin situproduction and release of heterologous antimicrobial effectors
Authors: Du Jiemin , Meile Susanne , Baggenstos Jasmin , Jäggi Tobias , Piffaretti Pietro , Hunold Laura , Matter Cassandra I. , Leitner Lorenz , Kessler Thomas M. , Loessner Martin J. , Kilcher Samuel , Dunne Matthew 
Published: March 2022

AbstractBacteriophages operate via pathogen-specific mechanisms of action distinct from conventional, broad-spectrum antibiotics and are emerging as promising alternatives. However, phage-mediated killing is often limited by bacterial resistance development (1,2). Here, we engineer phages for target-specific effector gene delivery and host-dependent production of colicin-like bacteriocins and cell wall hydrolases. Using urinary tract infection (UTI) as a model, we show how heterologous effector phage therapeutics (HEPTs) suppress resistance and improve uropathogen killing by dual phage- and effector-mediated targeting. Moreover, we designed HEPTs to control polymicrobial uropathogen communities through production of effectors with cross-genus activity. Using a phage-based companion diagnostic (3), we identified potential HEPT responder patients and treated their urineex vivo. Compared to wildtype phage, a colicin E7-producing HEPT demonstrated superior control of patientE. colibacteriuria. Arming phages with heterologous effectors paves the way for successful UTI treatment and represents a versatile tool to enhance and adapt phage-based precision antimicrobials.
BLMM: Parallelised Computing for Big Linear Mixed Models
Authors: Maullin-Sapey Thomas , Nichols Thomas E. 
Published: March 2022

AbstractWithin neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs. As sample sizes grow, researchers are presented with new opportunities to detect and account for grouping factors and covariance structure present in large experimental designs. In particular, standard linear model methods cannot account for the covariance and grouping structures present in large datasets, and the existing linear mixed models (LMM) tools are neither scalable nor exploit the computational speed-ups afforded by vectorisation of computations over voxels. Further, nearly all existing tools for imaging (fixed or mixed effect) do not account for variability in the patterns of missing data near cortical boundaries and the edge of the brain, and instead omit any voxels with any missing data. Yet in the large-n setting, such a voxel-wise deletion missing data strategy leads to severe shrinkage of the final analysis mask. To counter these issues, we describe the “Big” Linear Mixed Models (BLMM) toolbox, an efficient Python package for large-scale fMRI LMM analyses. BLMM is designed for use on high performance computing clusters and utilizes a Fisher Scoring procedure made possible by derivations for the LMM Fisher information matrix and score vectors derived in our previous work, Maullin-Sapey and Nichols (2021).
Accurate in silico confirmation of rare copy number variant calls from exome sequencing data using transfer learning
Authors: Tan Renjie , Shen Yufeng 
Published: March 2022

AbstractExome sequencing has been widely used in genetic studies of human diseases and clinical genetic diagnosis. Accurate detection of copy number variants (CNVs) is important to fully utilize exome sequencing data. However, due to the nature of noisy data, none of the existing methods can achieve high precision and high recall rate at the same time. A common practice is to perform filtration with quality metrics followed by manual inspection of read depth of candidate CNV regions. This approach does not scale in large studies. To address this issue, we present a deep transfer learning method, CNV-espresso, for confirming rare CNVs from exome sequencing data in silico. CNV-espresso encodes candidate CNV regions from exome sequencing data as images and uses convolutional neural networks to classify the image into different copy numbers. We trained and evaluated CNV-espresso on a large-scale offspring-parents trio exome sequencing dataset, using inherited CNVs in probands as positives and CNVs with mendelian errors as negatives. We further tested the performance using samples that have both exome and whole genome sequencing (WGS) data. Assuming the CNVs detected from WGS data as proxy of ground truth, CNV-espresso significantly improves precision while keeping recall almost intact, especially for CNVs that span small number of exons in exome data. We conclude that CNV-espresso is an effective method to replace most of manual inspection of CNVs in large-scale exome sequencing studies.
Automated classification of estrous stage in rodents using deep learning
Authors: Wolcott Nora S. , Sit Kevin K. , Raimondi Gianna , Hodges Travis , Shansky Rebecca M. , Galea Liisa A. M. , Ostroff Linnaea E. , Goard Michael J. 
Published: March 2022

ABSTRACTThe rodent estrous cycle modulates a range of biological functions, from gene expression to behavior. The cycle is typically divided into four stages, each characterized by distinct hormone concentration profiles. Given the difficulty of repeatedly sampling plasma steroid hormones from rodents, the primary method for classifying estrous stage is by identifying vaginal epithelial cell types. However, manual classification of epithelial cell samples is time-intensive and variable, even amongst expert investigators. Here, we use a deep learning approach to achieve classification accuracy at expert levels in a matter of seconds. Due to the heterogeneity and breadth of our input dataset, our deep learning approach (“EstrousNet”) is highly generalizable across rodent species, stains, and subjects. The EstrousNet algorithm exploits the temporal dimension of the hormonal cycle by fitting classifications to an archetypal estrous cycle, highlighting possible misclassifications and flagging anestrus phases (e.g., pseudopregnancy). EstrousNet allows for rapid estrous cycle staging, improving the ability of investigators to consider endocrine state in their rodent studies.
Cognitive penetrability of scene representations based on horizontal image disparities
Authors: Chen Yulan D. , Kaestner Milena , Norcia Anthony M. 
Published: March 2022

ABSTRACTThe structure of natural scenes is signaled by many visual cues. Principal amongst them are the binocular disparities created by the laterally separated viewpoints of the two eyes. Disparity cues are believed to be processed hierarchically, first in terms of local measurements of absolute disparity and second in terms of more global measurements of relative disparity that allow extraction of the depth structure of a scene. Psychophysical and oculomotor studies have suggested that relative disparities are particularly relevant to perception, whilst absolute disparities are not. Here, we compare neural responses to stimuli that isolate the absolute disparity cue with stimuli that contain additional relative disparity cues, using the high temporal resolution of EEG to determine the temporal order of absolute and relative disparity processing. By varying the observers’ task, we assess the extent to which each cue is cognitively penetrable. We find that absolute disparity is extracted before relative disparity, and that task effects arise only at or after the extraction of relative disparity. Our results indicate a hierarchy of disparity processing stages leading to the formation of a proto-object representation upon which higher cognitive processes can act.
Estimating contemporary migration numbers of adults based on kinship relationships in iteroparous species
Authors: Akita Tetsuya 
Published: March 2022

AbstractIn this paper, we describe the development of estimators for the contemporary migration number and rate of adults between two populations in iteroparous species. The proposed estimators are based on known half-sibling (HS) and/or parent–offspring (PO) relationships observed between the populations across breeding seasons. The rationale is that the HS and PO pairs exhibit information about the occurrence frequency of parental movements during the breeding interval. The proposed method allows for variance in the average number of off-spring per parent both within and between populations. In addition, coupled with the PO pairs found within the population, the estimators can be obtained from only genetic data. Generally, a sample size representing the square root of the population size is required to obtain meaningful information regarding migration. We describe a detailed evaluation of the performance of the estimators by running an individual-based model, and the results provide guidance regarding sample sizes to ensure the required accuracy and precision. Furthermore, given that there are few effective methods to estimate adult movement (especially when populations cannot be genetically distinct), we discuss the usefulness of this proposed kinship assignment method in terms of conservation biology and wildlife management.
Polyploidy and plant-fungus symbiosis: evidence of cytotype-specific microbiomes in the halophyteSalicornia(Amaranthaceae)
Authors: Gonçalves Danilo Reis , Pena Rodica , Albach Dirk C. 
Published: March 2022

AbstractPolyploidy is recognized as a mechanism of speciation in plants with cascading effects on biotic interactions. However, a limited number of studies have investigated the effects of polyploidy on the association of plants and microorganisms. Herein, we investigated whether twoSalicorniacytotypes (S. europaea– 2x andS. procumbens– 4x) show different root-associated fungal communities. Additionally, we explored the existence of cytotype-specific root anatomical traits, which could influence fungal recruitment and establishment.Salicorniaspp. were identified based on their ploidy level. The root-associated fungal microbiome ofSalicorniawas analyzed using high throughput amplicon sequencing (ITS1 of rDNA) in spring and summer. The following root anatomical traits were investigated: maximum root diameter, periderma thickness, parenchyma thickness, diameter of the vascular cylinder and maximum diameter of parenchyma cells. Our results showed that Shannon diversity and evenness indices were higher in samples ofSalicornia procumbens(4x) compared to those ofS. europaea(2x), and in summer the root-associated fungal community ofS. procumbens(4x) was significantly different from that ofS. europaea(2x). The ordersXylariales, MalassezialesandPleosporaleswere the most frequent root colonizers in both cytotypes and most of the taxa associated withSalicorniawere functionally classified as saprophytes or plant pathogens. Finally, we observed larger periderma and parenchyma layers inS. procumbens(4x) thanS. europaea(2x) that may contribute to the observed differences in community composition between the two cytotypes. Our results suggest that differences in ploidy may modulate plant interaction with fungi by affecting species recruitment and microbiome structure. In addition, cytotype-specific root traits may also have the potential to affect differently community assembly in the two cytotypes.
Generating minimum set of gRNA to cover multiple targets in multiple genomes with MINORg
Authors: Lee Rachelle R.Q. , Cher Wei Yuan , Chae Eunyoung 
Published: March 2022

AbstractMINORg is an offline gRNA design tool that generates the smallest possible combination of gRNA capable of covering all desired targets in multiple non-reference genomes. As interest in pangenomic research grows, so does the workload required for large screens in multiple individuals. MINORg aims to lessen this workload by capitalising on sequence homology to favour multi-target gRNA while simultaneously screening multiple genetic backgrounds in order to generate reusable gRNA panels. We demonstrated the practical application of MINORg by knocking out a 11 homologous genes tandemly arrayed in a multigene cluster in two Arabidopsis thaliana lineages using three gRNA output by MINORg. Source code is freely available at https://github.com/rlrq/MINORg.
Field and experimental data together with computational models reveal how cleaner fish adjust decisions in a biological market
Authors: Quiñones Andrés E. , Triki Zegni , Bshary Redouan 
Published: March 2022

AbstractWhile it is generally straightforward to quantify individual performance in cognitive experiments, identifying the underlying cognitive processes remains a major challenge. Often, different mechanistic underpinnings yield similar performances, and Lloyd Morgan’s cannon warrants acceptance of the simpler explanation. Alternatively, when the different mechanisms interact with environmental conditions, variation in performance across environments might allow to statistically infer the mechanism responsible. We illustrate this point by fitting computational models to experimental data on performance by wild-caught cleaner fishLabroides dimidiatusin an ephemeral reward task, as well as cleaner and client fish densities from the locations of capture. Using Bayesian statistics to fit the model parameters to performance data revealed that cleaner fish most likely estimate future consequences of an action, while it appears unlikely that the removal of the ephemeral reward acts as psychological punishment (negative reinforcement). Incorporating future consequences also yields performances that can be considered the result of locally optimal decision-rules, in contrast to the negative reinforcement mechanism. We argue that the combination of computational models with data is a powerful tool to infer the mechanistic underpinnings of cognitive performance.Author summaryPerformance in behavioural experiments is often used to assess the cognitive abilities of animals. However, animals can get to the same outcome in alternative ways. Thus, the outcome of the experiments does not tell us how animals achieve their performance. In order to overcome this limitation, we used a set of computational models, which provide predictions on how alternative cognitive mechanisms perform in the face of varying environmental conditions, and compare their predictions to performance data from individuals that experienced different conditions throughout their life. Our study system is the cleaner fishLabroides dimidiatus, a coral reef fish that feeds off ectoparasites and dead tissue from the skin of other reef fishes. Cleaner fish often have to choose among alternative clients seeking their cleaning service, and the optimal choice is often hard to achieve. Our performance experiments mimic the natural choices the cleaner fish make. The combination of experiments and computational models points to cleaner fish abilities to account for the future effect of their choices by estimating the long term expected value of those choices. Estimating long term value is a mechanism involved in human foresight.
Modelling the modulation of cortical Up-Down state switching by astrocytes
Authors: Moyse Lisa Blum , Berry Hugues 
Published: March 2022

AbstractConverging experimental reports have shown that the firing dynamics of neural networks in several cortical brain areas can exhibit Up-Down synchronization regimes, spontaneously alternating between long periods of high collective firing activity (Up state) and long periods of relative silence (Down state). The molecular or cellular mechanisms that support the emergence of these reversible transitions are still uncertain. In addition to intrinsic mechanisms supported by the local neurons of the network, recent experimental studies have suggested that the astrocytes of the local network can actually control the emergence of Up-Down regimes. Here we propose and study a neural network model to explore the implication of astrocytes in this dynamical phenomenon. We consider three populations of cells: excitatory neurons, inhibitory neurons and astrocytes, interconnected by gliotransmission events, from neurons to astrocytes and back. We derive two models for this three-population system: a rate model and a stochastic spiking neural network with thousands of neurons and astrocytes. In numerical simulations of these three-population models, the presence of astrocytes is indeed observed to promote the emergence of Up-Down regimes with realistic characteristics. Linear stability analysis reveals that astrocytes in these models do not change the bifurcation structure of these systems, but change the localization of the bifurcations in the parameter space. Accordingly, with the addition of astrocytes, the network can enter a bistability region of the dynamics, where the Up-Down dynamical regime emerges. Simulations of the stochastic network model further evidence that astrocytes provide a stationary and stable background of gliotransmission events to the neurons, that triggers spontaneous transitions between synchronized Up and Down phases of neuronal firing. Taken together, our work provides a theoretical framework to test scenarios and hypotheses on the modulation of Up-Down dynamics by gliotransmission from astrocytes.
Domain-general and domain-specific functional networks of Broca’s area underlying language processing
Authors: Bulut Talat 
Published: March 2022

AbstractDespite abundant research on the role of Broca’s area in language processing, there is still no consensus on language specificity of this region and its connectivity network. The present study employed the meta-analytic connectivity modeling procedure to identify and compare domain-specific (language-related) and domain-general (non-language) functional connectivity patterns of three subdivisions within the broadly defined Broca’s area: pars opercularis (IFGop), pars triangularis (IFGtri) and pars orbitalis (IFGorb) of the left inferior frontal gyrus. The findings revealed a left-lateralized frontotemporal network for all regions of interest underlying domain-specific linguistic functions. The domain-general network and its conjunction with the domain-specific network, however, spanned frontoparietal regions that overlap with the multiple-demand network. The findings suggest that language-specificity of Broca’s area emerges within a left-lateralized frontotemporal network, and that domain-general resources are garnered from the frontoparietal network when required by task demands.
Risk of ESKD in related older living kidney donors
Authors: Arking Andrew , Ammary Fawaz Al , Kaddu Gabriella , Massie Allan B. , Segev Dorry L. , Garonzik-Wang Jacqueline , Muzaale Abimereki D. 
Published: March 2022

ABSTRACTLiving kidney donors who are biologically related to the recipient have higher risk for end-stage kidney disease (ESKD) compared with those who are unrelated to the recipient. This risk is greater for first-degree relatives than more distant relatives. To understand if this holds true for older donors, who were cleared for donation and might be past the peak-age for hereditary disease, we used donor data (SRTR) linked to ESKD registry data (CMS) and stratified donors by age (younger vs older [≥50 years]) and race (black, Hispanic, and white). Younger related donors of all racial groups had higher risk of ESKD compared with younger unrelated donors; however, only older related white and Hispanic donors had higher risk of ESRD compared with unrelated older donors (2.3-fold for white full-siblings and 1.9-fold for white parents/offspring; 3.3-fold for Hispanic full-siblings and 2.0-fold for Hispanic parents/offspring). Older related black donors did not have higher risk compared to older unrelated black donors (0.8-fold for black full-siblings and 0.5-fold for black parents/offspring). Our study points to an earlier age of onset of kidney disease in black donors with a family history of ESKD. Our findings call for programs that promote living donation among related older black donor candidates.