Predicting state transitions in the transcriptome and metabolome using a linear dynamical system model

<p>Abstract</p> <p>Background</p> <p>Modelling of time series data should not be an approximation of input data profiles, but rather be able to detect and evaluate dynamical changes in the time series data. Objective criteria that can be used to evaluate dynamical changes in data are therefore important to filter experimental noise and to enable extraction of unexpected, biologically important information.</p> <p>Results</p> <p>Here we demonstrate the effectiveness of a Markov model, named the Linear Dynamical System, to simulate the dynamics of a transcript or metabolite time series, and propose a probabilistic index that enables detection of time-sensitive changes. This method was applied to time series datasets from <it>Bacillus subtilis </it>and <it>Arabidopsis thaliana </it>grown under stress conditions; in the former, only gene expression was studied, whereas in the latter, both gene expression and metabolite accumulation. Our method not only identified well-known changes in gene expression and metabolite accumulation, but also detected novel changes that are likely to be responsible for each stress response condition.</p> <p>Conclusion</p> <p>This general approach can be applied to any time-series data profile from which one wishes to identify elements responsible for state transitions, such as rapid environmental adaptation by an organism.</p

Strong Emission-Line Galaxies at Low Redshift in the Field around the Quasar SDSSp J104433.04-012502.2

We discuss observational properties of strong emission-line galaxies at low redshift found by our deep imaging survey for high-redshift Ly alpha emitters. In our surveys, we used the narrowband filter, NB816 (lambda_center=8150A with FWHM = 120A), and the intermediate-band filter, IA827 (lambda_center = 8270A with FWHM = 340A). In this survey, 62 NB816-excess (> 0.9 mag) and 21 IA827-excess (> 0.8 mag) objects were found. Among them, we found 20 NB816-excess and 4 IA827-excess Ly alpha emitter candidates. Therefore, it turns out that 42 NB816-excess and 17 IA827-excess objects are strong emission-line objects at lower redshift. Since 4 objects in the two low-z samples are common, the total number of strong low-z emitters is 55. Applying our photometric redshift technique, we identify 7 H alpha emitters at z~0.24, 20 H beta-[OIII] ones at z~0.65, and 11 [OII] ones at z~1.19. However, we cannot determine reliable photometric redshifts of the remaining 17 emitters. The distributions of their rest frame equivalent widths are consistently understood with recent studies of galaxy evolution from z~1 to z~0.Comment: 28 pages, 8 figures, PASJ, Vol. 58, No. 1, in pres

A survey of NB921 dropouts in the Subaru Deep Field

In order to search for high-redshift galaxies beyond $z = 6.6$ in the Subaru Deep Field, we have investigated NB921-dropout galaxies where NB921 is the narrowband filter centered at 919.6 nm with FWHM of 13.2 nm for the Suprime-Cam on the Subaru Telescope. There are no secure NB921-dropout candidates brighter than $z^\prime = 25.5$. Based on this result, we discuss the UV luminosity function of star-forming galaxies at $z > 6.6$.Comment: 10 pages, 5 figures, PASJ, Vol.57, No.5, in pres

トウケイテキ シュホウ ニ モトズク イデンシ ハツゲン ノ ジケイレツ カイセキ ニ カンスル ケンキュウ

https://library.naist.jp/mylimedio/dllimedio/show.cgi?bookid=100045247&oldid=83976博士 (Doctor)理学 (Science)博第473号甲第473号博士（理学）奈良先端科学技術大学院大

Role of linkage structures in supply chain for managing greenhouse gas emissions

Abstract This study describes a structural decomposition analysis (SDA) of Japanese greenhouse gas (GHG) emissions from 1990 to 2005, focusing on four linkage structures in the Leontief inverse representing supply chains in Japan. The developed RAS-invariant decomposition was applied to Japanese linked input–output tables for the three 5-year periods studied. It examined the effect of the Leontief inverse on emissions changes into the specific effects of forward linkage, backward linkage, the average of forward/backward linkage and kernel structure. Our SDA method solves the problem of parameter independence completely. The accuracy of those effects has been improved mathematically compared with conventional methods. For example, it was detected that backward linkage contributes to an increase in GHG emissions, while conventional methods erroneously determine a decrease. The results of the SDA confirmed that forward linkage and kernel structure contributed to a rise in GHG emissions, and that backward linkage consistently increased emissions in the three periods. Some sectors have robust linkage in the supply chain with consistently increasing emissions, which should be preferentially improved to mitigate their indirect GHG emissions in Japan

MOESM1 of Role of linkage structures in supply chain for managing greenhouse gas emissions

Additional file 1. Numerical examples with Leontief inverse and List of sector names

Nutrient-extended input–output (NutrIO) method for the food nitrogen footprint

Agro-food systems require nutrient input from several sources to provide food products and food-related services. Many of the nutrients are lost to the environment during supply chains, potentially threatening human and ecosystem health. Countries therefore need to reduce their nutrient/nitrogen footprints. These footprints are importantly affected by links between sectors. However, existing assessments omit the links between sectors, especially between the agriculture, manufacturing, and energy sectors. We propose a novel approach called the nutrient-extended input–output (NutrIO) method to determine the nutrient footprint as a sum of direct and indirect inputs throughout the supply chains from different sources of nutrients. The NutrIO method is based on a nutrient-based material flow analysis linked to economic transactions. Applying this method, we estimated the nitrogen footprint of Japan in 2011 at 21.8 kg-N capita ^−1 yr ^−1 : 9.7 kg-N capita ^−1 yr ^−1 sourced from new nitrogen for agriculture and fisheries, 7.0 kg-N capita ^−1 yr ^−1 from recycled nitrogen as organic fertilizers, and 5.1 kg-N capita ^−1 yr ^−1 from industrial nitrogen for chemical industries other than fertilizers. A further annexed 55.4 kg-N capita ^−1 yr ^−1 of unintended nitrogen input was sourced from fossil fuels for energy production. The nitrogen intensity of the wheat and barley cultivation sector, at 1.50 kg-N per thousand Japanese yen (JPY) production, was much higher than that of the 0.12 kg-N per thousand JPY production for the rice cultivation sector. Industrial nitrogen accounted for 2%–7% of the nitrogen footprint of each major food-related sector. The NutrIO nitrogen footprint sourced from new nitrogen for agriculture and fisheries, at 8.6 kg-N capita ^−1 yr ^−1 for domestic final products, is comparable to the food nitrogen footprint calculated by other methods, at 8.5–10.5 kg-N capita ^−1 yr ^−1 . The NutrIO method provides quantitative insights for all stakeholders of food consumption and production to improve the nutrient use efficiencies of agro-food supply chains

Dynamics of Time-Lagged Gene-to-Metabolite Networks of Escherichia coli Elucidated by Integrative Omics Approach

In the postgenomics era, integrative analysis of several “omics” data is absolutely required for understanding the cell as a system. Integrative analysis of transcriptomics and metabolomics can lead to elucidation of gene-to-metabolite networks. When integrating different time series “omics” data, it is necessary to take into consideration a time lag between those data. In the present study, we conducted an integrative analysis of time series transcriptomics and metabolomics data of Escherichia coli generated by cDNA microarray and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS), respectively. We identified a 60-min time lag between transition points of transcriptomics and metabolomics data by using a Linear Dynamical System. Furthermore, we investigated gene-to-metabolite correlations in the context of time lag, obtained the maximum number of correlated pairs at transcripts leading 60-min time lag, and finally revealed gene-to-metabolite relations in the phospholipid biosynthesis pathway. Taking into consideration the time lag between transcriptomics and metabolomics data in time series analysis could unravel novel gene-to-metabolite relations. According to gene-to-metabolite correlations, phosphatidylglycerol plays a more critical role for membrane balance than phosphatidylethanolamine in E. coli