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An, L.

Publications and source records attributed to An, L..

At least 55 records · Page 3Linked to original sources
bioRxiv2023

Keystone taxa responsible for the microbial community stability and performance of activated sludges

BackgroundThe functions and stability of a community depend on its species, which form complex interaction networks. The keystone taxa identified by network analysis are generally considered to play a vital role in the structure and function of microbial communities, but there is no uniformly accepted operational definition of such taxa. Further, what species and how they affect the communitys stability and function are still poorly understood. MethodsTo solve this problem, we performed a large-scale network analysis of the microbial communities residing in 1186 activated sludge (AS) samples. ResultsWe found that the AS co-occurrence network is a typical scale-free network. While most taxa in the AS co-occurrence network have little association, there are still a small number of taxa that are strongly interconnected. We defined a group of keystone taxa that have an important impact on network stability. Further analysis results indicate that the communities harboring the keystone taxa maintain higher stability, but these communities possess lower pollutant removal rates. In addition, we found that keystone taxa were more likely to appear in samples with lower sludge load. ConclusionsOur work identified the keystone taxa that maintain the stability of microbial communities in the AS systems but at the cost of reducing their function. This finding shed light on the relationship between composition, stability, and function within microbial communities. It also provides novel insights into manipulating the function of microbial communities by modifying their composition.

ecology↗
bioRxiv2023

High immigration rates critical for establishing emigration-driven diversity in microbial communities

Unraveling the mechanisms governing the diversity of ecological communities is a central goal in ecology. While microbial dispersal (including the emigration and immigration process) constitutes an important ecological process, the coupling effects of dispersal and microbial competition in microbial diversity are poorly understood. Here, we investigated how microbial dispersal affects the diversity of microbial communities in the presence of inter-species competition, using a generalized Lotka-Volterra model in combination with experimental investigations. Our model shows that emigration reduces the diversity induced by immigration at low immigration rates. We surprisingly find that it increases the diversity of the community when the immigration rates cross a defined threshold, which we identified as Ineutral. We also found that at high immigration rates, emigration weakens the relative abundance of fast-growing species, and thus enhances the mass effect and increases the diversity. We experimentally confirmed this finding using cocultures of 20 bacterial strains isolated from the soil. Our model further showed that Ineutral exists over a wide range of species pool sizes, growth rates, and interspecies interactions, and decreases with the increasing of species pool size, growth rate, and interspecies interaction. Our work deepens the understanding of the effects of dispersal on diversity of natural communities.

ecology↗
bioRxiv2022

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local- global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (GITHUB_LINK).

neuroscience↗
bioRxiv2022

Hallucination of closed repeat proteins containing central pockets

In pseudocyclic proteins such as TIM barrels, {beta} barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity which can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in E. coli are consistent with the design models, and two crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small molecule binders or enzymes.

molecular biology↗
bioRxiv2022

Goal-specific brain MRI harmonization

There is significant interest in pooling magnetic resonance image (MRI) data from multiple datasets to enable mega-analysis. Harmonization is typically performed to reduce heterogeneity when pooling MRI data across datasets. Most MRI harmonization algorithms do not explicitly consider downstream application performance during harmonization. However, the choice of downstream application might influence what might be considered as study-specific confounds. Therefore, ignoring downstream applications during harmonization might potentially limit downstream performance. Here we propose a goal-specific harmonization framework that utilizes downstream application performance to regularize the harmonization procedure. Our framework can be integrated with a wide variety of harmonization models based on deep neural networks, such as the recently proposed conditional variational autoencoder (cVAE) harmonization model. Three datasets from three different continents with a total of 2787 participants and 10085 anatomical T1 scans were used for evaluation. We found that cVAE removed more dataset differences than the widely used ComBat model, but at the expense of removing desirable biological information as measured by downstream prediction of mini mental state examination (MMSE) scores and clinical diagnoses. On the other hand, our goal-specific cVAE (gcVAE) was able to remove as much dataset differences as cVAE, while improving downstream cross-sectional prediction of MMSE scores and clinical diagnoses.

neuroscience↗
bioRxiv2022

Comparison of Two Individual Identification Algorithms for Snow Leopards (Panthera uncia) after Automated Detection

1. Photo-identification of individual snow leopards (Panthera uncia) is the primary technique for density estimation for the species. A high volume of images from multiple projects, combined with pre-existing historical catalogs, has made identifying snow leopard individuals within the images cost- and time-intensive. 2. To speed the classification among a high volume of photographs, we trained and evaluated image classification methods for PIE v2 (a triplet loss network), and we compared PIEs accuracy to the HotSpotter algorithm (a SIFT based algorithm). Analyzed data were collected from a curated catalog of free-ranging snow leopards photographed across years (2012-2019) in Afghanistan and from samples in captivity provided by zoos from Finland, Sweden, Germany, and the United States. 3. Results show that PIE outperforms HotSpotter. We also found weaknesses in the initial PIE model, like a minor amount of background matching, which was addressed, although likely not fully resolved, by applying background subtraction (BGS) and left-right mirroring (LR) methods. The PIE BGS LR model combined with Hotspotter showed a Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. 4. Overall, our results recommend implementing PIE v2 simultaneously with HotSpotter on Whiskerbook.org.

bioinformatics↗
bioRxiv2022

Prelimbic proBDNF facilitates memory destabilization by regulation of neuronal function in juveniles

Fear regulation changes as a function of age and adolescence is a key developmental period for the continued maturation of fear neural circuitry. The involvement of prelimbic proBDNF in fear memory extinction and its mediated signaling were reported previously. Given the inherent high level of proBDNF during juvenile period, we tested whether prelimbic proBDNF regulated synaptic and neuronal functions allowing to influencing retrieval-dependent memory processing. By examining freezing behavior of auditory fear conditioned rats, we found high levels of prelimbic proBDNF in juvenile rats enhanced destabilization of the retrieval-dependent weak but not strong fear memory through activating p75NTR-GluN2B signaling. This modification was attributed to the increment in proportion of thin type spine and promotion in synaptic function, as evidence by facilitation of NMDA-mediated EPSCs and GluN2B-dependent synaptic depression. The strong prelimbic theta- and gamma-oscillation coupling predicted the suppressive effect of proBDNF on the recall of post-retrieval memory. Our results critically emphasize the importance of developmental proBDNF for modification of retrieval-dependent memory and provide a potential critical targeting to inhibit threaten memories associated with neurodevelopment disorders.

neuroscience↗
bioRxiv2021

Defining the Sensitivity Landscape of 74,389 EGFR Variants to Tyrosine Kinase Inhibitors

BackgroundTyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with advanced non-small-cell lung carcinoma (NSCLC) when activating epidermal growth factor receptor (EGFR) mutations are detected. However, except for the well-studied EGFR mutations, most EGFR mutations lack treatment regimens. MethodsWe constructed two EGFR variant libraries containing substitutions, deletions, or insertions using the saturation mutagenesis method. All the variants were located in the EGFR mutation hotspot (exons 18-21). The sensitivity of these variants to afatinib, erlotinib, gefitinib, icotinib, and osimertinib was systematically studied by determining their enrichment in massively parallel cytotoxicity assays using an endogenous EGFR-depleted cell line, PC9. ResultsA total of 3,914 and 70,475 variants were detected in the constructed EGFR Substitution-Deletion (Sub-Del) and exon 20 Insertion (Ins) libraries, accounting for 99.3% and 55.8% of the designed variants, respectively. Of the 3,914 Sub-Del variants, 813 were highly enriched in the reversible TKI (erlotinib, gefitinib, icotinib) cytotoxicity assays and 51 were enriched in the irreversible TKI (afatinib, osimertinib) cytotoxicity assays. For the 70,475 Ins variants, insertions at amino acid positions 770-774 were highly enriched in all the five TKI cytotoxicity assays. Moreover, the top 5% of the enriched insertion variants included a glycine or serine insertion at high frequency. ConclusionsWe present a comprehensive reference for the sensitivity of EGFR variants to five commonly used TKIs. The approach used here should be applicable to other genes and targeted drugs.

genetics↗
bioRxiv2021

Integration of embryo-endosperm interaction into a holistic and dynamic picture of seed development using a rice mutant with notched-belly grains

The interaction between the embryo and endosperm affects seed development, an essential process in yield formation in crops such as rice. Signals that communicate between embryo and endosperm are largely unknown. Here we use the notched-belly (NB) mutant with impaired communication between embryo and endosperm to evaluate 1) the impact of embryo on developmental staging of the endosperm; 2) signaling pathways emanating from the embryo that regulate endosperm development. Hierachical clustering of mRNA datasets from embryo and endosperm samples collected through development in NB and wild type showed a delaying effect of the embryo on the developmental transition of the endosperm by extending the middle stage. K-means clustering further identified coexpression modules of gene sets specific for embryo and endosperm development. Combined gene expression and biochemical analysis showed that T6P-SnRK1, gibberellin and auxin signalling from the embryo regulate endosperm developmental transition. The data enable us to propose a new seed developmental staging system for rice and the most detailed signature of rice grain formation to date, that will direct genetic strategies for rice yield improvement.

plant biology↗
bioRxiv2020

Meta-matching: a simple framework to translate phenotypic predictive models from big to small data

There is significant interest in using brain imaging data to predict non-brain-imaging phenotypes in individual participants. However, most prediction studies are underpowered, relying on less than a few hundred participants, leading to low reliability and inflated prediction performance. Yet, small sample sizes are unavoidable when studying clinical populations or addressing focused neuroscience questions. Here, we propose a simple framework - "meta-matching" - to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in boutique studies. The key observation is that many large-scale datasets collect a wide range inter-correlated phenotypic measures. Therefore, a unique phenotype from a boutique study likely correlates with (but is not the same as) some phenotypes in some large-scale datasets. Meta-matching exploits these correlations to boost prediction in the boutique study. We applied meta-matching to the problem of predicting non-brain-imaging phenotypes using resting-state functional connectivity (RSFC). Using the UK Biobank (N = 36,848), we demonstrated that meta-matching can boost the prediction of new phenotypes in small independent datasets by 100% to 400% in many scenarios. When considering relative prediction performance, meta-matching significantly improved phenotypic prediction even in samples with 10 participants. When considering absolute prediction performance, meta-matching significantly improved phenotypic prediction when there were least 50 participants. With a growing number of large-scale population-level datasets collecting an increasing number of phenotypic measures, our results represent a lower bound on the potential of meta-matching to elevate small-scale boutique studies.

neuroscience↗
bioRxiv2019

Predicting Alzheimer’s disease progression using deep recurrent neural networks

Early identification of individuals at risk of developing Alzheimers disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimers Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al. 2018) from the Alzheimers Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a "preprocessing" issue, by imputing the missing data using the previous timepoint ("forward filling") or linear interpolation ("linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing ("model filling"). Our analyses suggest that the minimalRNN with "model filling" compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020.

neuroscience↗
bioRxiv2019

Microbial Community Dissimilarity for Source Tracking with Application in Forensic Studies

Microbial source-tracking is a useful tool for trace evidence analysis in Forensics. Community-wide massively parallel sequencing profiles can bypass the need for satellite microbes or marker sets which are unreliable when handling unstable samples. We propose a method utilizing Aitchison distance to select important suspects/sources and then propose several methods to estimate the proportions of microbial communities/samples coming from important suspects/sources. A series of comprehensive simulation studies show that these methods are capable of accurate selection and improve the performance of current methods Bayesian SourceTracker and FEAST in the presence of noise microbial sources.

bioinformatics↗
bioRxiv2019

scDoc: Correcting Drop-out Events in Single-cell RNA-seq Data

Single-cell RNA sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of \"drop-out\" events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this paper, we present a novel Single-Cell RNA-seq Drop-Out Correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells. scDoc is the first method that involves drop-out information to account for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc can impute the drop-out events more accurately and robustly; specifically, it outperforms all available imputation methods in reference to data visualization, cell subpopulation identification, and differential expression detection in scRNA-seq data.

bioinformatics↗
bioRxiv2019

An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis

Thousands of epigenomic datasets have been generated in the past decade, but it is difficult for researchers to effectively utilize all the data relevant to their projects. Systematic integrative analysis can help meet this need, and the VISION project was established for ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis. Here, we systematically integrated extensive data recording epigenetic features and transcriptomes from many sources, including individual laboratories and consortia, to produce a comprehensive view of the regulatory landscape of differentiating hematopoietic cell types in mouse. By employing IDEAS as our Integrative and Discriminative Epigenome Annotation System, we identified and assigned epigenetic states simultaneously along chromosomes and across cell types, precisely and comprehensively. Combining nuclease accessibility and epigenetic states produced a set of over 200,000 candidate cis-regulatory elements (cCREs) that efficiently capture enhancers and promoters. The transitions in epigenetic states of these cCREs across cell types provided insights into mechanisms of regulation, including decreases in numbers of active cCREs during differentiation of most lineages, transitions from poised to active or inactive states, and shifts in nuclease accessibility of CTCF-bound elements. Regression modeling of epigenetic states at cCREs and gene expression produced a versatile resource to improve selection of cCREs potentially regulating target genes. These resources are available from our VISION website (usevision.org) to aid research in genomics and hematopoiesis.

genomics↗
bioRxiv2019

High innate preference for black substrate in the chive gnat, Bradysia odoriphaga (Diptera: Sciaridae)

The chive gnat, Bradysia odoriphaga, is a notorious pest of Allium species in China. Colour trapping is an established method for monitoring and controlling of Bradysia species. In order to clarify the effect of colour preference of B. odoriphaga for the egg-laying substrate, multiple-choice tests were employed to assess the spontaneous response of the chive gnat to different colour hues and brightness levels under different intensities of white illumination and two spectrally different illuminations. Given the choice among four colours differing in hue under different intensities of white illumination and two spectrally different illuminations, chive gnat adults visited preferably the black substrate, a lesser extent to brown and green substrates, and the least extent to orange substrate irrespective of illumination. Given the choice among four levels of brightness under the same illumination conditions as those in the previous experiment (different intensities of white illumination and two spectrally different illuminations), chive gnats preferred black substrate over dark grey, and these over light grey and white substrates. Meanwhile, both virgin and copulated adults significantly preferred black over other colour hues and brightness. Based on our results, we conclude that the chive gnat adults significantly prefer black substrates irrespective of colour hues and brightness. This behaviour does not alter due to ambient light condition changes. No difference observed between choices of female and male adults. Our results provide new insight for understanding the colour choice behaviour in chive gnat and pave a way to improve monitoring and control of chive gnats and management.\n\nSummary statementChive gnat (Bradysia odoriphaga) innately prefer to move to black substrate irrespective of colour hues and brightness. This behaviour maintained the ambient lights change.

animal behavior and cognition↗
bioRxiv2018

S3norm: simultaneous normalization of sequencing depth and signal-to-noise ratio in epigenomic data

Quantitative comparison of epigenomic data across multiple cell types or experimental conditions is a promising way to understand the biological functions of epigenetic modifications. However, differences in sequencing depth and signal-to-noise ratios in the data from different experiments can hinder our ability to identify real biological variation from raw epigenomic data. Proper normalization is required prior to data analysis to gain meaningful insights. Most existing methods for data normalization standardize signals by rescaling either background regions or peak regions, assuming that the same scale factor is applicable to both background and peak regions. While such methods adjust for differences in sequencing depths, they do not address differences in the signal-to-noise ratios across different experiments. We developed a new data normalization method, called S3norm, that normalizes the sequencing depths and signal-to-noise ratios across different data sets simultaneously by a monotonic nonlinear transformation. We show empirically that the epigenomic data normalized by our method, compared to existing methods, can better capture real biological variation, such as impact on gene expression regulation.

bioinformatics↗
bioRxiv2018

The rat medial prefrontal cortex exhibits flexible neural activity states during the performance of an odor span task

Medial prefrontal cortex (mPFC) activity is fundamental for working memory (WM), attention, and behavioral inhibition; however, a comprehensive understanding of the neural computations underlying these processes is still forthcoming. Towards this goal, neural recordings were obtained from the mPFC of awake, behaving rats performing an odor span task of WM capacity. Neural populations were observed to encode distinct task epochs and the transitions between epochs were accompanied by abrupt shifts in neural activity patterns. Putative pyramidal neuron activity increased significantly earlier in the delay for sessions where rats achieved higher spans. Furthermore, increased putative interneuron activity was only observed at the termination of the delay thus indicating that local processing in inhibitory networks was a unique feature to initiate foraging. During foraging, changes in neural activity patterns associated with the approach to a novel odor, but not familiar odors, were robust. Collectively, these data suggest that distinct mPFC activity states underlie the delay, foraging, and reward epochs of the odor span task. Transitions between these states enable successful performance in dynamic environments placing strong demands on the substrates of working memory.

neuroscience↗