Biology subjects

Neupane, S.

Publications and source records attributed to Neupane, S..

27 records · Page 2Linked to original sources

bioRxiv2022

Vector production via mental navigation in the entorhinal cortex

A cognitive map is a suitably structured representation that enables an agent to perform novel computations using prior experience, for instance planning a new route in a familiar space1,2. Recent work in mammals has found direct evidence for such structured representations in the presence of exogenous sensory inputs in both spatial3,4 and non-spatial domains5-15. Here, we test a foundational postulate of the original cognitive map theory1,16 that cognitive maps are recruited endogenously during mental navigation without external input. We recorded from the entorhinal cortex of monkeys in a mental navigation task that required animals to use a joystick to produce one-dimensional vectors between pairs of visual landmarks without sensory feedback about the intermediate landmarks. Animals ability to perform the task and generalize to new pairs indicated that they relied on a structured representation of the landmarks. Task-modulated neurons exhibited periodicity and ramping that matched the temporal structure of the landmarks. Neuron pairs with high periodicity scores had invariant cross-correlation structure, a signature of grid cell continuous attractor states17- 19. A basic continuous attractor network model of path integration20 augmented with a Hebbian learning mechanism provided an explanation of how the system endogenously recalls landmarks. The model also made an unexpected prediction that endogenous landmarks transiently slow down path integration, reset the dynamics, and thereby, reduce variability. Remarkably, this prediction was borne out of a reanalysis of behavior. Together, our findings connect the structured activity patterns in the entorhinal cortex to the endogenous recruitment of a cognitive map during mental navigation.

neuroscience↗

bioRxiv2022

Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multi-environment trials

Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and/or areas where new crops have been introduced, e.g., lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multi-environment dataset, phenotyped for phenology-related traits across nine locations and over three years, along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions) which has also been genotyped with an exome capture array. Genomewide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.

genetics↗

bioRxiv2022

Tracking the dynamics of perisaccadic visual signals with magnetoencephalography

Many brain functions are difficult to localize, as they involve distributed networks that reconfigure themselves on short timescales. One example is the integration of oculomotor and visual signals that occurs with each eye movement: The brain must combine motor signals about the eye displacement with retinal signals, to infer the structure of the surrounding environment. Our understanding of this process comes primarily from single-neuron recordings, which are limited in spatial extent, or fMRI measurements, which have poor temporal resolution. We have therefore studied visual processing during eye movements, using magnetoencephalography (MEG), which affords high spatiotemporal resolution. Human subjects performed a task in which they reported the orientation of a visual stimulus while executing a saccade. After removal of eye movement artifacts, time-frequency analysis revealed a signal that propagated in the beta-frequency band from parietal cortex to visual cortex. This signal had the characteristics of perisaccadic "remapping", a neural signature of the integration of oculomotor and visual signals. These results reveal a novel mechanism of visual perception and demonstrate that MEG can provide a useful window into distributed brain functions.

neuroscience↗

bioRxiv2020

STRATEGIC IDENTIFICATION OF NEW GENETIC DIVERSITY TO EXPAND LENTIL (Lens culinaris Medik.) PRODUCTION USING NEPAL AS AN EXAMPLE

This article is written to disseminate descriptive results obtained from field experiments conducted at Bardiya, Nepal, during winter 2016 and 2017 on 324 diverse lentil (L. culinaris Medik.) genotypes obtained from genebanks and breeding programs around the world. Lentil genotypes were examined for phenological traits related to adaptation, and recommendations for potential incorporation of new exotic genotypes into Nepalese breeding programs are presented. In addition, predictions of days to flowering based on temperature and photoperiod are used to identify genotypes that hold suitability for specific growing regions in Nepal, allowing for the potential expansion of growing regions. From this study we found many potentially adapted genotypes for terai, mid-hill and high hill growing regions, the list consists of a number of large-seeded lines with yellow cotyledons, an entirely new market class of lentils in Nepal. This paper primarily targets lentil breeders and agronomists; furthermore, it can be equally informative to extension workers involved in the pulse crop research and development in Nepal and other countries with similar climatic conditions.

plant biology↗

bioRxiv2020

Characterization of Induced Susceptibility Effects of Soybean Aphid on Soybean

Soybean aphid (SBA) is one of the major pests of soybean (Glycine max) in the United States of America. The main objective of this research was to characterize interactions between two different biotypes of soybean aphids in susceptible and resistant soybean cultivars. Demographic and transcriptomic responses of susceptible and resistant (Rag1) soybean cultivars to aphid feeding were investigated in soybean plants colonized by aphids (biotype 1) in the presence or absence of inducer population (biotype 2) at day 1 and day 11. Leaves tissues collected at day 1 and day 11 post infestation were used for RNA sequencing, and ten RNA datasets with 266,535,654 sequence reads were analyzed. In the presence of inducer population, we found 746 and 243 differentially expressed genes (DEGs) in susceptible and resistant cultivars, respectively at day 1, whereas 981 and 377 DEGs were found in susceptible and resistant cultivars, respectively at day 11. Enrichment analysis showed a response to chitin, lignin catabolic and metabolic process, asparagine metabolic process, response to chemical unique to treatment with no inducer population, whereas, response to reactive oxygen species, photosynthesis, regulation of endopeptidase activity unique to treatment with inducer population. Furthermore, 14 DEGs were observed in Rag QTLs regions, particularly six DEGs in Rag1 containing QTL. The identified DEGs in the experiment in both resistant and susceptible cultivars during the interaction of soybean and SBA are potential candidates for furthering investigation into induced susceptibility.

plant biology↗

bioRxiv2020

Adaptive divergence of meiotic recombination rate in ecological speciation

Theories predict that directional selection during adaptation to a novel habitat results in elevated meiotic recombination rate. Yet the lack of population-level recombination rate data leaves this hypothesis untested in natural populations. Here we examine the population-level recombination rate variation in two incipient ecological species, the microcrustacean Daphnia pulex (an ephemeral-pond species) and D. pulicaria (a permanent-lake species). The divergence of D. pulicaria from D. pulex involved habitat shifts from pond to lake habitats as well as strong local adaptation due to directional selection. Using a novel single-sperm genotyping approach, we estimated the male-specific recombination rate of two linkage groups in multiple populations of each species in common garden experiments and identified a significantly elevated recombination rate in D. pulicaria. Most importantly, population genetic analyses show that the divergence in recombination rate between these two species is most likely due to divergent selection in distinct ecological habitats rather than neutral evolution. Significance statementWhether directional selection during adaptation to a novel habitat results in elevated meiotic recombination remains largely untested in natural populations. This work examines the population-level recombination rate in two closely related microcructacean species Daphnia pulex and D. pulicaria using single-sperm genotyping approach. Recombination rate data from two linkage groups show elevated recombination rates in D. pulicaria whose divergence from D. pulex is accompanied by a habitat shift. Importantly, population genetic analysis suggests that this divergence of recombination is likely adaptive rather than neutral.

evolutionary biology↗

bioRxiv2020

Understanding photothermal interactions will help expand production range and increase genetic diversity of lentil (Lens culinaris Medik.)

O_LILentil (Lens culinaris Medik.) is cultivated under a wide range of environmental conditions, which led to diverse phenological adaptations and resulted in a decrease in genetic variability within breeding programs due to reluctance in using genotypes from other environments. C_LIO_LIWe phenotyped 324 genotypes across nine locations over three years to assess their phenological response to the environment of major lentil production regions and to predict days from sowing to flowering (DTF) using a photothermal model. C_LIO_LIDTF was highly influenced by the environment and is sufficient to explain adaptation. We were able to predict DTF reliably in most environments using a simple photothermal model, however, in certain site-years, results suggest there may be additional environmental factors at play. Hierarchical clustering of principal components revealed the presence of eight groups based on the responses of DTF to contrasting environments. These groups are associated with the coefficients of the photothermal model and revealed differences in temperature and photoperiod sensitivity. C_LIO_LIExpanding genetic diversity is critical to the success of a breeding program; understanding adaptation will facilitate the use of exotic germplasm. Future climate change scenarios will result in increase temperature and/or shifts in production areas, we can use the photothermal model to identify genotypes most likely to succeed in these new environments. C_LI

plant biology↗

bioRxiv2019

Genomic selection for lentil breeding: empirical evidence

Genomic selection (GS) is a type of marker-based selection which was initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models and approaches have been developed to implement GS; however, none of these methods have been tested for use in lentil breeding. This study was conducted to evaluate different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single-trait models, two multiple-trait models, and models that account for population structure and genotype-by-environment interaction (GEI) using a lentil diversity panel and two recombinant inbred lines (RIL) populations that were genotyped using a custom exome capture assay. Within-population, across-population and across-environment predictions were made for five phenology traits. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, traits, and statistical models. Single-trait models showed similar accuracy for each trait in the absence of large effect QTL but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and multiple-trait (MT) models increased prediction accuracy for a low heritability trait by up to 66% and 14% but accuracy did not improve for traits of high heritability. Moderate to high accuracies were obtained for within-population and across-environment predictions but across-population prediction accuracy was very low. This suggests that GS can be implemented in lentil to make predictions within populations and across environments, but across-population prediction should not be considered when the population size is small.

genomics↗

bioRxiv2018

Assessing Combinability of Phylogenomic Data using Bayes Factors

With the rapid reduction in sequencing costs of high-throughput genomic data, it has become commonplace to use hundreds of genes/sites to infer phylogeny of any study system. While sampling large number of genes has given us a tremendous opportunity to uncover previously unknown relationships and improve phylogenetic resolution, it also presents us with new challenges when the phylogenetic signal is confused by differences in the evolutionary histories of sampled genes. Given the addition of accurate marginal likelihood estimation methods into popular Bayesian software programs, it is natural to consider using the Bayes Factor (BF) to compare different partition models in which genes within any given partition subset share both tree topology and edge lengths. We explore using marginal likelihood to assess data subset combinability when data subsets have varying levels of phylogenetic discordance due to deep coalescence events among genes (simulated within a species tree), and compare the results with our recently-described phylogenetic informational dissonance index (D) estimated for each data set. BF effectively detects phylogenetic incongruence, and provides a way to assess the statistical significance of D values. We discuss methods for calibrating BFs, and use calibrated BFs to assess data combinability using an empirical data set comprising 56 plastid genes from green algae order Volvocales.

evolutionary biology↗