scholarly journals Spatiotemporally Heterogeneous Population Dynamics of Gut Bacteria Inferred from Fecal Time Series Data

mBio ◽  
2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Hidetoshi Inamine ◽  
Stephen P. Ellner ◽  
Peter D. Newell ◽  
Yuan Luo ◽  
Nicolas Buchon ◽  
...  
Keyword(s):  
Time Series ◽  
Gut Microbiome ◽  
Time Series Data ◽  
Bacterial Species ◽  
Microbial Populations ◽  
Series Data ◽  
Food Bolus ◽  
Ecological Processes ◽  
Intact Cells ◽  
Fecal Samples

ABSTRACT A priority in gut microbiome research is to develop methods to investigate ecological processes shaping microbial populations in the host from readily accessible data, such as fecal samples. Here, we demonstrate that these processes can be inferred from the proportion of ingested microorganisms that is egested and their egestion time distribution, by using general mathematical models that link within-host processes to statistics from fecal time series. We apply this framework to Drosophila melanogaster and its gut bacterium Acetobacter tropicalis. Specifically, we investigate changes in their interactions following ingestion of a food bolus containing bacteria in a set of treatments varying the following key parameters: the density of exogenous bacteria ingested by the flies (low/high) and the association status of the host (axenic or monoassociated with A. tropicalis). At 5 h post-ingestion, ~35% of the intact bacterial cells have transited through the gut with the food bolus and ~10% are retained in a viable and culturable state, leaving ~55% that have likely been lysed in the gut. Our models imply that lysis and retention occur over a short spatial range within the gut when the bacteria are ingested from a low density, but more broadly in the host gut when ingested from a high density, by both gnotobiotic and axenic hosts. Our study illustrates how time series data complement the analysis of static abundance patterns to infer ecological processes as bacteria traverse the host. Our approach can be extended to investigate how different bacterial species interact within the host to understand the processes shaping microbial community assembly. IMPORTANCE A major challenge to our understanding of the gut microbiome in animals is that it is profoundly difficult to investigate the fate of ingested microbial cells as they travel through the gut. Here, we created mathematical tools to analyze microbial dynamics in the gut from the temporal pattern of their abundance in fecal samples, i.e., without direct observation of the dynamics, and validated them with Drosophila fruit flies. Our analyses revealed that over 5 h after ingestion, most bacteria have likely died in the host or have been egested as intact cells, while some living cells have been retained in the host. Bacterial lysis or retention occurred across a larger area of the gut when flies ingest bacteria from high densities than when flies ingest bacteria from low densities. Our mathematical tools can be applied to other systems, including the dynamics of gut microbial populations and communities in humans.

scholarly journals Stem girth changes in response to soil water potential in lowland dipterocarp forest in Borneo: a time-series analysis

2020 ◽  
Author(s):  
D. M. Newbery ◽  
M. Lingenfelder
Keyword(s):  
Time Series ◽  
Soil Water ◽  
Water Relations ◽  
Time Series Data ◽  
Soil Water Potential ◽  
Series Data ◽  
Relative Depth ◽  
Ecological Processes ◽  
Flux Dynamics ◽  
Negative Relationships

AbstractTime series data offer a way of investigating the causes driving ecological processes. To test for possible differences in water relations between species of different forest structural guilds, daily stem girth increments (gthi), of 18 trees across six species were regressed individually on soil moisture potential (SMP) and temperature (TEMP), accounting for temporal autocorrelation (in GLS-arima models), and compared between a wet and a dry period. Coefficients were estimates of response in gthi to increasing SMP or TEMP. The best-fitting significant variables were SMP the day before and TEMP the same day. The first resulted in a mix of positive and negative coefficients, the second largely positive ones. Negative relationships for large canopy trees can be interpreted in a reversed causal sense: fast transporting stems depleted soil water and lowered SMP. Positive relationships for understorey trees meant they took up most water at high SMP. The unexpected negative relationships for these understorey trees may have been due to their roots accessing deeper water supplies (SMP being inversely related to that of the surface layer), this influenced by competition with larger neighbour trees. A tree-soil flux dynamics manifold may have been operating. Patterns of mean diurnal girth variation were more consistent among species than, but weakly related to, time-series coefficients, suggesting no simple trait-based differentiation of responses. Expected differences in response to SMP in the wet and dry periods did not support a previous hypothesis for drought and non-drought tolerant understorey guilds. Trees within species showed highly individual responses. Time-series gthi-SMP regressions might be applied as indicators of relative depth of access to water for small trees. Obtaining detailed information on individual tree’s root systems and recording SMP at many depths and locations are needed to get closer to the mechanisms that underlie complex tree-soil water relations in tropical forests.

scholarly journals Inference and Validation of The Structure of Lotka-Volterra Models

2021 ◽  
Author(s):  
Eberhard Voit ◽  
Jacob Davis ◽  
Daniel Olivenca
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
The Other ◽  
Microbial Populations ◽  
Series Data ◽  
Observation Data ◽  
Volterra Models ◽  
Rich Data ◽  
Over Time ◽  
Mixed Community

Abstract For close to a century, Lotka-Volterra (LV) models have been used to investigate interactions among populations of different species. For a few species, these investigations are straightforward. However, with the arrival of large and complex microbiomes, unprecedently rich data have become available and await analysis. In particular, these data require us to ask which microbial populations of a mixed community affect other populations, whether these influences are activating or inhibiting and how the interactions change over time. Here we present two new inference strategies for interaction parameters that are based on a new algebraic LV inference (ALVI) method. One strategy uses different survivor profiles of communities grown under similar conditions, while the other pertains to time series data. In addition, we address the question of whether observation data are compliant with the LV structure or require a richer modeling format.

scholarly journals A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2312
Author(s):  
Junming Yang ◽  
Yunjun Yao ◽  
Yongxia Wei ◽  
Yuhu Zhang ◽  
Kun Jia ◽  
...  
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Land Surface ◽  
Time Series Data ◽  
Temporal Variations ◽  
Series Data ◽  
Surface Reflectance ◽  
Spatiotemporal Resolution ◽  
Ecological Processes ◽  
Clustering Model

The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.

scholarly journals Reverse-engineering ecological theory from data

2018 ◽  
Vol 285 (1878) ◽  
pp. 20180422 ◽  
Author(s):  
Benjamin T. Martin ◽  
Stephan B. Munch ◽  
Andrew M. Hein
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Theory Development ◽  
Theoretical Models ◽  
Series Data ◽  
Ecological Interactions ◽  
Dynamical Equations ◽  
Ecological Processes ◽  
Functional Forms ◽  
Dynamics Of Populations

Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. competition, predation) believed to govern system dynamics. The inverse of this approach—inferring which processes and ecological interactions drive observed dynamics—remains an open problem in ecology. Here, we propose a way to attack this problem using a machine learning method known as symbolic regression, which seeks to discover relationships in time-series data and to express those relationships using dynamical equations. We found that this method could rapidly discover models that explained most of the variance in three classic demographic time series. More importantly, it reverse-engineered the models previously proposed by theoretical ecologists to describe these time series, capturing the core ecological processes these models describe and their functional forms. Our findings suggest a potentially powerful new way to merge theory development and data analysis.

scholarly journals Inference and Validation of the Structure of Lotka-Volterra Models

2021 ◽  
Author(s):  
Eberhard Voit ◽  
Jacob Davis ◽  
Daniel Olivenca
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
The Other ◽  
Microbial Populations ◽  
Series Data ◽  
Observation Data ◽  
Volterra Models ◽  
Rich Data ◽  
Over Time ◽  
Mixed Community

For close to a century, Lotka-Volterra (LV) models have been used to investigate interactions among populations of different species. For a few species, these investigations are straightforward. However, with the arrival of large and complex microbiomes, unprecedently rich data have become available and await analysis. In particular, these data require us to ask which microbial populations of a mixed community affect other populations, whether these influences are activating or inhibiting and how the interactions change over time. Here we present two new inference strategies for interaction parameters that are based on a new algebraic LV inference (ALVI) method. One strategy uses different survivor profiles of communities grown under similar conditions, while the other pertains to time series data. In addition, we address the question of whether observation data are compliant with the LV structure or require a richer modeling format.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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