High sensitivity CRDS of CO2 in the 1.18 µm transparency window. Validation tests of current spectroscopic databases

2020 ◽  
Vol 247 ◽  
pp. 106942 ◽  
Author(s):  
E.V. Karlovets ◽  
S. Kassi ◽  
A. Campargue
Keyword(s):  
High Sensitivity ◽  
Transparency Window ◽  
Validation Tests

High sensitivity CRDS of CO 2 in the 1.74 µm transparency window. A validation test for the spectroscopic databases

2018 ◽  
Vol 207 ◽  
pp. 95-103 ◽  
Author(s):  
P. Čermák ◽  
E.V. Karlovets ◽  
D. Mondelain ◽  
S. Kassi ◽  
V.I. Perevalov ◽  
...  
Keyword(s):  
High Sensitivity ◽  
Transparency Window ◽  
Validation Test

scholarly journals ANALYSIS OF FENPROPATHRIN, LAMBDA-CYHALOTHRIN, AND CHLOROTHALONIL IN POTATO AND TOMATO SAMPLES USING GAS CHROMATOGRAPHY WITH AN ELECTRON CAPTURE DETECTOR

2018 ◽  
Vol 10 (1) ◽  
pp. 70
Author(s):  
Nadia Rahmiani ◽  
Harmita . ◽  
Herman Suryadi
Keyword(s):  
Gas Chromatography ◽  
Electron Capture ◽  
Limit Of Detection ◽  
High Sensitivity ◽  
Electron Capture Detector ◽  
Gas Chromatograph ◽  
Vegetable Sample ◽  
Lambda Cyhalothrin ◽  
Limit Of Quantitation ◽  
Validation Tests

Objective: This study aimed to analyze pesticide contents in potato and tomato samples.Methods: In the present study, we determined the presence of the pesticides fenpropathrin, lambda-cyhalothrin, and chlorothalonil in conventionaland organic potatoes and tomatoes using a gas chromatograph equipped with an electron capture detector and validated the associated methods.Acetone-based extraction was performed using the Dutch mini-Luke method with minimal weights and volumes.Results: Validation tests showed a range of 70–120% and precision of ≤20%, and linearity tests on the three standard pesticides gave r values of≥0.9990 for all three pesticides. Limit of detection and limit of quantitation values showed high sensitivity, although in vegetable sample analyses,none of the three pesticides were detected.Conclusion: Our data show that the chosen method for analysis of the pesticides fenpropathrin, lambda-cyhalothrin, and chlorothalonil in potatoesand tomatoes is valid and that the marketed potatoes and tomatoes meet the SNI 7313: 2008 standard for “Maximum Limits of Pesticide Residues onAgricultural Products” and the associated Japanese standards

High sensitivity ICLAS of H218O in the 12,580–13,550cm−1 transparency window

2011 ◽  
Vol 112 (6) ◽  
pp. 913-924 ◽  
Author(s):  
Olga M. Leshchishina ◽  
Olga V. Naumenko ◽  
Alain Campargue
Keyword(s):  
High Sensitivity ◽  
Transparency Window

scholarly journals NmSEER V2.0: a prediction tool for 2′-O-methylation sites based on random forest and multi-encoding combination

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Yiran Zhou ◽  
Qinghua Cui ◽  
Yuan Zhou
Keyword(s):  
Random Forest ◽  
Prediction Model ◽  
Learning Algorithm ◽  
High Sensitivity ◽  
Sequence Information ◽  
Sequence Profile ◽  
Single Nucleotide ◽  
Nucleotide Resolution ◽  
Validation Tests ◽  
Single Nucleotide Resolution

Abstract Background 2′-O-methylation (2′-O-me or Nm) is a post-transcriptional RNA methylation modified at 2′-hydroxy, which is common in mRNAs and various non-coding RNAs. Previous studies revealed the significance of Nm in multiple biological processes. With Nm getting more and more attention, a revolutionary technique termed Nm-seq, was developed to profile Nm sites mainly in mRNA with single nucleotide resolution and high sensitivity. In a recent work, supported by the Nm-seq data, we have reported a method in silico for predicting Nm sites, which relies on nucleotide sequence information, and established an online server named NmSEER. More recently, a more confident dataset produced by refined Nm-seq was available. Therefore, in this work, we redesigned the prediction model to achieve a more robust performance on the new data. Results We redesigned the prediction model from two perspectives, including machine learning algorithm and multi-encoding scheme combination. With optimization by 5-fold cross-validation tests and evaluation by independent test respectively, random forest was selected as the most robust algorithm. Meanwhile, one-hot encoding, together with position-specific dinucleotide sequence profile and K-nucleotide frequency encoding were collectively applied to build the final predictor. Conclusions The predictor of updated version, named NmSEER V2.0, achieves an accurate prediction performance (AUROC = 0.862) and has been settled into a brand-new server, which is available at http://www.rnanut.net/nmseer-v2/ for free.

The CO2 absorption spectrum in the 2.3 µm transparency window by high sensitivity CRDS: (I) Rovibrational lines

2016 ◽  
Vol 184 ◽  
pp. 233-240 ◽  
Author(s):  
S. Vasilchenko ◽  
M. Konefal ◽  
D. Mondelain ◽  
S. Kassi ◽  
P. Čermák ◽  
...  
Keyword(s):  
Absorption Spectrum ◽  
High Sensitivity ◽  
Transparency Window ◽  
Co2 Absorption
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