Genome- and Community-Level Interaction Insights into Carbon Utilization and Element Cycling Functions of Hydrothermarchaeota in Hydrothermal Sediment

ABSTRACT Hydrothermal vents release reduced compounds and small organic carbon compounds into the surrounding seawater, providing essential substrates for microbial growth and bioenergy transformations. Despite the wide distribution of the marine benthic group E archaea (referred to as Hydrothermarchaeota) in the hydrothermal environment, little is known about their genomic repertoires and biogeochemical significance. Here, we studied four highly complete (>80%) metagenome-assembled genomes (MAGs) from a black smoker chimney and the surrounding sulfur-rich sediments on the South Atlantic Mid-Ocean Ridge and publicly available data sets (the Integrated Microbial Genomes system of the U.S. Department of Energy-Joint Genome Institute and NCBI SRA data sets). Genomic analysis suggested a wide carbon metabolic diversity of Hydrothermarchaeota members, including the utilization of proteins, lactate, and acetate; the anaerobic degradation of aromatics; the oxidation of C1 compounds (CO, formate, and formaldehyde); the utilization of methyl compounds; CO2 incorporation by the tetrahydromethanopterin-based Wood-Ljungdahl pathway; and participation in the type III ribulose-1,5-bisphosphate carboxylase/oxygenase-based Calvin-Benson-Bassham cycle. These microbes also potentially oxidize sulfur, arsenic, and hydrogen and engage in anaerobic respiration based on sulfate reduction and denitrification. Among the 140 MAGs reconstructed from the black smoker chimney microbial community (including Hydrothermarchaeota MAGs), community-level metabolic predictions suggested a redundancy of carbon utilization and element cycling functions and interactive syntrophic and sequential utilization of substrates. These processes might make various carbon and energy sources widely accessible to the microorganisms. Further, the analysis suggested that Hydrothermarchaeota members contained important functional components obtained from the community via lateral gene transfer, becoming a distinctive clade. This might serve as a niche-adaptive strategy for metabolizing heavy metals, C1 compounds, and reduced sulfur compounds. Collectively, the analysis provides comprehensive metabolic insights into the Hydrothermarchaeota. IMPORTANCE This study provides comprehensive metabolic insights into the Hydrothermarchaeota from comparative genomics, evolution, and community-level perspectives. Members of the Hydrothermarchaeota synergistically participate in a wide range of carbon-utilizing and element cycling processes with other microorganisms in the community. We expand the current understanding of community interactions within the hydrothermal sediment and chimney, suggesting that microbial interactions based on sequential substrate metabolism are essential to nutrient and element cycling.

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Genome and community-level interaction insights on wide carbon utilizing and element cycling function of Hydrothermarchaeota from hydrothermal sediment

10.1101/768564 ◽

2019 ◽

Author(s):

Zhichao Zhou ◽

Yang Liu ◽

Wei Xu ◽

Jie Pan ◽

Zhu-Hua Luo ◽

...

Keyword(s):

Comparative Genomics ◽

Microbial Interactions ◽

Community Level ◽

Reduced Sulfur Compounds ◽

Community Interactions ◽

Element Cycling ◽

Bisphosphate Carboxylase ◽

Hydrothermal Sediment ◽

Wide Range ◽

Functional Components

AbstractHydrothermal vents release reduced compounds and small organic carbons into surrounding seawaters, providing essential substrates for microbial-derived biosynthesis and bioenergy transformations. Despite the wide distribution of Marine Benthic Group-E archaea (referred to as Hydrothermarchaeota) in hydrothermal environments, little is known on their genome blueprints and ecofunctions. Here, we studied four relatively high-completeness (> 80%) metagenome-assembled genomes (MAGs) from a black smoker chimney and surrounding sulfide sediments in the Mid-Atlantic Ridge of the South Atlantic Ocean (BSmoChi-MAR) as well as publicly available datasets. Comparative genomics suggest that Hydrothermarchaeota members have versatile carbon metabolism, including assimilating proteins, lactate and acetate, degrading aromatics anaerobically, oxidizing C1 compounds (CO, formate, and formaldehyde), utilizing methyl-compounds, and incorporating CO2 by tetrahydromethanopterin-based Wood–Ljungdahl (WL) pathway and Calvin–Benson–Bassham (CBB) cycle with type III Ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO). They could oxidize sulfur, arsenic, and hydrogen, and respire anaerobically via sulfate reduction and denitrification based on genomic evidence. The redundancy of carbon utilizing and element cycling functions, and the interactive processes of syntrophic and sequential utilization of substrates from community-level metabolic prediction, enable wide accessibility of carbon and energy sources to microorganisms. Hydrothermarchaeota members derived important functional components from the community through lateral gene transfer, and became clade-distinctive on genome content, which might serve as a niche-adaptive strategy to metabolize potential heavy metals, C1 compounds, and reduced sulfur compounds.ImportanceThis study provides comprehensive metabolic insights on Hydrothermarchaeota from comparative genomics, evolution and community-level aspects. Hydrothermarchaeota synergistically participates in a wide range of carbon utilizing and element cycling processes with other microbes in the community. We expand the current understanding of community interactions within hydrothermal sediment environments, suggesting that microbial interactions driven by functions are essential to nutrient and element cycling.

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mtDNAcombine: tools to combine sequences from multiple studies

BMC Bioinformatics ◽

10.1186/s12859-021-04048-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Eleanor F. Miller ◽

Andrea Manica

Keyword(s):

Sequence Data ◽

Data Extraction ◽

Bayesian Skyline Plot ◽

Model Organisms ◽

Data Sets ◽

Data Handling ◽

Online Database ◽

Genetic Studies ◽

Wide Range ◽

Existing Data

Abstract Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.

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MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

BMC Bioinformatics ◽

10.1186/s12859-021-04288-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yance Feng ◽

Lei M. Li

Keyword(s):

Biological Significance ◽

Housekeeping Genes ◽

R Package ◽

Data Sets ◽

Statistical Regression ◽

Rna Seq ◽

Least Trimmed Squares ◽

Standard Data ◽

Wide Range ◽

Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

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A Visual and VAE Based Hierarchical Indoor Localization Method

Sensors ◽

10.3390/s21103406 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3406

Author(s):

Jie Jiang ◽

Yin Zou ◽

Lidong Chen ◽

Yujie Fang

Keyword(s):

Image Retrieval ◽

Indoor Localization ◽

Data Sets ◽

Indoor Environments ◽

Global Features ◽

Data Set ◽

Data Annotation ◽

Wide Range ◽

Annotation Costs ◽

Global And Local

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

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An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies

BioMed Research International ◽

10.1155/2016/6741418 ◽

2016 ◽

Vol 2016 ◽

pp. 1-18 ◽

Cited By ~ 5

Author(s):

Mustafa Yuksel ◽

Suat Gonul ◽

Gokce Banu Laleci Erturkmen ◽

Ali Anil Sinaci ◽

Paolo Invernizzi ◽

...

Keyword(s):

Real Life ◽

Case Series ◽

Background Information ◽

Data Sets ◽

Local Data ◽

Lombardy Region ◽

Common Information ◽

Spontaneous Reports ◽

Wide Range ◽

Common Information Model

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.

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Considerations of the Scale of Radiocarbon Offsets in the East Mediterranean, and Considering a Case for the Latest (Most Recent) Likely Date for the Santorini Eruption

Radiocarbon ◽

10.1017/s0033822200047202 ◽

2012 ◽

Vol 54 (3-4) ◽

pp. 449-474 ◽

Cited By ~ 13

Author(s):

Sturt W Manning ◽

Bernd Kromer

Keyword(s):

Time Horizon ◽

Weighted Average ◽

17Th Century ◽

Data Sets ◽

East Mediterranean ◽

Average Value ◽

Accuracy And Precision ◽

Wide Range ◽

The Impact ◽

East Mediterranean Region

The debate over the dating of the Santorini (Thera) volcanic eruption has seen sustained efforts to criticize or challenge the radiocarbon dating of this time horizon. We consider some of the relevant areas of possible movement in the14C dating—and, in particular, any plausible mechanisms to support as late (most recent) a date as possible. First, we report and analyze data investigating the scale of apparent possible14C offsets (growing season related) in the Aegean-Anatolia-east Mediterranean region (excluding the southern Levant and especially pre-modern, pre-dam Egypt, which is a distinct case), and find no evidence for more than very small possible offsets from several cases. This topic is thus not an explanation for current differences in dating in the Aegean and at best provides only a few years of latitude. Second, we consider some aspects of the accuracy and precision of14C dating with respect to the Santorini case. While the existing data appear robust, we nonetheless speculate that examination of the frequency distribution of the14C data on short-lived samples from the volcanic destruction level at Akrotiri on Santorini (Thera) may indicate that the average value of the overall data sets is not necessarily the most appropriate14C age to use for dating this time horizon. We note the recent paper of Soter (2011), which suggests that in such a volcanic context some (small) age increment may be possible from diffuse CO2emissions (the effect is hypothetical at this stage and hasnotbeen observed in the field), and that "if short-lived samples from the same stratigraphic horizon yield a wide range of14C ages, the lower values may be the least altered by old CO2." In this context, it might be argued that a substantive “low” grouping of14C ages observable within the overall14C data sets on short-lived samples from the Thera volcanic destruction level centered about 3326–3328 BP is perhaps more representative of the contemporary atmospheric14C age (without any volcanic CO2contamination). This is a subjective argument (since, in statistical terms, the existing studies using the weighted average remain valid) that looks to support as late a date as reasonable from the14C data. The impact of employing this revised14C age is discussed. In general, a late 17th century BC date range is found (to remain) to be most likelyeven ifsuch a late-dating strategy is followed—a late 17th century BC date range is thus a robust finding from the14C evidence even allowing for various possible variation factors. However, the possibility of a mid-16th century BC date (within ∼1593–1530 cal BC) is increased when compared against previous analyses if the Santorini data are considered in isolation.

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Multi-instrument gravity-wave measurements over Tierra del Fuego and the Drake Passage – Part 1: Potential energies and vertical wavelengths from AIRS, COSMIC, HIRDLS, MLS-Aura, SAAMER, SABER and radiosondes

Atmospheric Measurement Techniques ◽

10.5194/amt-9-877-2016 ◽

2016 ◽

Vol 9 (3) ◽

pp. 877-908 ◽

Cited By ~ 17

Author(s):

Corwin J. Wright ◽

Neil P. Hindley ◽

Andrew C. Moss ◽

Nicholas J. Mitchell

Keyword(s):

Gravity Wave ◽

Wave Packets ◽

Lower Thermosphere ◽

Lower Troposphere ◽

Drake Passage ◽

Tierra Del Fuego ◽

Data Sets ◽

Meteor Radar ◽

Diverse Range ◽

Wide Range

Abstract. Gravity waves in the terrestrial atmosphere are a vital geophysical process, acting to transport energy and momentum on a wide range of scales and to couple the various atmospheric layers. Despite the importance of these waves, the many studies to date have often exhibited very dissimilar results, and it remains unclear whether these differences are primarily instrumental or methodological. Here, we address this problem by comparing observations made by a diverse range of the most widely used gravity-wave-resolving instruments in a common geographic region around the southern Andes and Drake Passage, an area known to exhibit strong wave activity. Specifically, we use data from three limb-sounding radiometers (Microwave Limb Sounder, MLS-Aura; HIgh Resolution Dynamics Limb Sounder, HIRDLS; Sounding of the Atmosphere using Broadband Emission Radiometry, SABER), the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) GPS-RO constellation, a ground-based meteor radar, the Advanced Infrared Sounder (AIRS) infrared nadir sounder and radiosondes to examine the gravity wave potential energy (GWPE) and vertical wavelengths (λz) of individual gravity-wave packets from the lower troposphere to the edge of the lower thermosphere ( ∼  100 km). Our results show important similarities and differences. Limb sounder measurements show high intercorrelation, typically > 0.80 between any instrument pair. Meteor radar observations agree in form with the limb sounders, despite vast technical differences. AIRS and radiosonde observations tend to be uncorrelated or anticorrelated with the other data sets, suggesting very different behaviour of the wave field in the different spectral regimes accessed by each instrument. Evidence of wave dissipation is seen, and varies strongly with season. Observed GWPE for individual wave packets exhibits a log-normal distribution, with short-timescale intermittency dominating over a well-repeated monthly-median seasonal cycle. GWPE and λz exhibit strong correlations with the stratospheric winds, but not with local surface winds. Our results provide guidance for interpretation and intercomparison of such data sets in their full context.

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Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning

Brain Informatics ◽

10.1186/s40708-021-00149-x ◽

2022 ◽

Vol 9 (1) ◽

Author(s):

Marcos Fabietti ◽

Mufti Mahmud ◽

Ahmad Lotfi

Keyword(s):

Machine Learning ◽

Open Access ◽

Short Term Memory ◽

The Body ◽

Data Sets ◽

Additional Information ◽

Machine Learning Model ◽

Signal Characteristics ◽

Wide Range ◽

Memory Network

AbstractAcquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.

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Comparative Cladistics: Fossils, Morphological Data Partitions and Lost Branches in the Fossil Tree of Life

10.31237/osf.io/7sa6d ◽

2017 ◽

Author(s):

Ross Mounce

Keyword(s):

Large Scale ◽

Phylogenetic Signal ◽

Scientific Progress ◽

Molecular Data ◽

Morphological Data ◽

Data Sets ◽

Phylogenetic Groups ◽

Use Of Data ◽

Wide Range ◽

Cladistic Analyses

In this thesis I attempt to gather together a wide range of cladistic analyses of fossil and extant taxa representing a diverse array of phylogenetic groups. I use this data to quantitatively compare the effect of fossil taxa relative to extant taxa in terms of support for relationships, number of most parsimonious trees (MPTs) and leaf stability. In line with previous studies I find that the effects of fossil taxa are seldom different to extant taxa – although I highlight some interesting exceptions. I also use this data to compare the phylogenetic signal within vertebrate morphological data sets, by choosing to compare cranial data to postcranial data. Comparisons between molecular data and morphological data have been previously well explored, as have signals between different molecular loci. But comparative signal within morphological data sets is much less commonly characterized and certainly not across a wide array of clades. With this analysis I show that there are many studies in which the evidence provided by cranial data appears to be be significantly incongruent with the postcranial data – more than one would expect to see just by the effect of chance and noise alone. I devise and implement a modification to a rarely used measure of homoplasy that will hopefully encourage its wider usage. Previously it had some undesirable bias associated with the distribution of missing data in a dataset, but my modification controls for this. I also take an in-depth and extensive review of the ILD test, noting it is often misused or reported poorly, even in recent studies. Finally, in attempting to collect data and metadata on a large scale, I uncovered inefficiencies in the research publication system that obstruct re-use of data and scientific progress. I highlight the importance of replication and reproducibility – even simple reanalysis of high profile papers can turn up some very different results. Data is highly valuable and thus it must be retained and made available for further re-use to maximize the overall return on research investment.

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Exploring the Association Between the “Big Five” Personality Traits and Fatal Opioid Overdose: County-Level Empirical Analysis (Preprint)

10.2196/preprints.24939 ◽

2020 ◽

Author(s):

Zhasmina Tacheva ◽

Anton Ivanov

Keyword(s):

United States ◽

Big Five ◽

Psychological Factors ◽

The United States ◽

Community Level ◽

Opioid Overdose ◽

County Level ◽

Psychological Traits ◽

Wide Range ◽

The Relationship

BACKGROUND Opioid-related deaths constitute a problem of pandemic proportions in the United States, with no clear solution in sight. Although addressing addiction—the heart of this problem—ought to remain a priority for health practitioners, examining the community-level psychological factors with a known impact on health behaviors may provide valuable insights for attenuating this health crisis by curbing risky behaviors before they evolve into addiction. OBJECTIVE The goal of this study is twofold: to demonstrate the relationship between community-level psychological traits and fatal opioid overdose both theoretically and empirically, and to provide a blueprint for using social media data to glean these psychological factors in a real-time, reliable, and scalable manner. METHODS We collected annual panel data from Twitter for 2891 counties in the United States between 2014-2016 and used a novel data mining technique to obtain average county-level “Big Five” psychological trait scores. We then performed interval regression, using a control function to alleviate omitted variable bias, to empirically test the relationship between county-level psychological traits and the prevalence of fatal opioid overdoses in each county. RESULTS After controlling for a wide range of community-level biopsychosocial factors related to health outcomes, we found that three of the operationalizations of the five psychological traits examined at the community level in the study were significantly associated with fatal opioid overdoses: extraversion (β=.308, P<.001), neuroticism (β=.248, P<.001), and conscientiousness (β=.229, P<.001). CONCLUSIONS Analyzing the psychological characteristics of a community can be a valuable tool in the local, state, and national fight against the opioid pandemic. Health providers and community health organizations can benefit from this research by evaluating the psychological profile of the communities they serve and assessing the projected risk of fatal opioid overdose based on the relationships our study predict when making decisions for the allocation of overdose-reversal medication and other vital resources.

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