Using Real-Time Recording to Enhance the Analysis of Within-Session Functional Analysis Data

Comprehensive sampling design including determination of the distribution, number of observations and specification of desired levels of precision is typically ignored in time studies of yarding operations. A computer-based data collection, processing, and analysis system was developed for time studies that permits improved sampling design. Data collection programs were written for continuous timing of yarding operations and downloaded on to hand-held computers equipped with real-time clocks. After each shift of observation, the time study data are uploaded to a portable microcomputer. The data are then processed through a series of programs that provide error checking, cataloging, and formatting in preparation for analysis. Data from three cable yarding operations were used in a study to assess the potential for sequential design and to determine precision levels obtainable from short-duration time studies. Confidence intervals were computed cumulatively and used to assess whether additional observation of a specific machine on a particular site was justified. The data collection, processing, and real-time analysis system shows promise as a technique for improving sampling design of time studies for timber harvesting operations through sequential analysis of the data.

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Near real-time adjusted reanalysis forcing data for hydrology

10.5194/hess-2017-326 ◽

2017 ◽

Author(s):

Peter Berg ◽

Chantal Donnelly ◽

David Gustafsson

Keyword(s):

Real Time ◽

Arctic Region ◽

Analysis Data ◽

Global Scale ◽

Current Data ◽

The Arctic ◽

Data Sets ◽

Initial State ◽

Time Step ◽

Data Set

Abstract. Updating climatological forcing data to near current data are compelling for impact modelling, e.g. to update model simulations or to simulate recent extreme events. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of daily resolution data at a global scale has previously been solved by adjusting re-analysis data global gridded observations. However, existing data sets of this type have been produced for a fixed past time period, determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between present and the end of the data set. Further, hydrological forecasts require initialisations of the current state of the snow, soil, lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present a method named GFD (Global Forcing Data) to combine different data sets in order to produce near real-time updated hydrological forcing data that are compatible with the products covering the climatological period. GFD resembles the already established WFDEI method (Weedon et al., 2014) closely, but uses updated climatological observations, and for the near real-time it uses interim products that apply similar methods. This allows GFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the GFD method and different produced data sets, which are evaluated against the WFDEI data set, as well as with hydrological simulations with the HYPE model over Europe and the Arctic region. We show that GFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data, although less well for the last two months of the updating cycle. For real-time updates until the current day, extending GFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

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Multi-tissue transcriptome analysis using hybrid-seq revealed potential genes and biological pathways associated with azadirachtin A biosynthesis in neem (Azadirachtin indica)

10.21203/rs.2.23446/v1 ◽

2020 ◽

Author(s):

Huiyan Wang ◽

Ning Wang ◽

Yixin Huo

Keyword(s):

Real Time ◽

Single Molecule ◽

Biosynthetic Pathway ◽

De Novo ◽

Analysis Data ◽

Sequencing Data ◽

Neem Tree ◽

Illumina Hiseq ◽

Oxidosqualene Cyclase ◽

Azadirachtin A

Abstract Background: Azadirachtin A is a triterpenoid from neem tree exhibiting excellent activities against over 600 insect species in agriculture. The manufacture of azadirachtin A depends on extraction from neem tissues, which is not ecofriendly and sustainable. The low yield and discontinuous supply impeded the further application. The biosynthetic pathway of azadirachtin A is still well-known.Results: We attempted to explore azadirachtin A biosynthetic pathway and identified key involved genes by analyzing transcriptome data of five neem tissues through hybrid-seq (Illumina HiSeq and Pacific Biosciences Single Molecule Real Time (PacBio SMRT)) technology. A total 219 and 397 up-regulated differentially expressed genes (DEGs) in leaf and fruit tissues than other tissues (root, stem and flower) were isolated. After phylogenetic analysis and domain prediction, 22 candidates encoding 2,3-oxidosqualene cyclase (OSC), alcohol dehydrogenase (ADH), cytochrome P450 (CYP450), acyltransferase (ACT) and esterase (EST) proposed to be involved in azadirachtin A biosynthesis were finally selected. De novo assembled sequences were verified by Quantitative Real-Time PCR (qRT-PCR) analysis.Conclusions: By integrating and analysis data from Illumina HiSeq and PacBio SMRT platform, 22 DEGs were finally selected as candidates involved in azadirachtin A biosynthesis. The obtained reliable and accurate sequencing data provided important novel information for understanding neem genome. Our data shed new light on the understanding of other triterpenoids biosynthesis in neem trees and provide reference for exploring other valuable natural product biosynthesis in plants.

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