scholarly journals An Evaluation of Leaf Biomass : Length Ratio as a Tool for Nondestructive Assessment in Eelgrass (Zostera marinaL.)

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Hector Echavarria-Heras ◽  
Elena Solana-Arellano ◽  
Kun-Seop Lee ◽  
Shinya Hosokawa ◽  
Ernesto Franco-Vizcaíno
Keyword(s):  
Allometric Scaling ◽  
Leaf Growth ◽  
Threshold Value ◽  
Leaf Length ◽  
Length Ratio ◽  
Leaf Biomass ◽  
Data Sets ◽  
Length Measurements ◽  
Using Data

The characterization of biomass and its dynamics provides valuable information for the assessment of natural and transplanted eelgrass populations. The need for simple, nondestructive assessments has led to the use of the leaf biomass-to-length ratio for converting leaf-length measurements, which can be easily obtained, to leaf growth rates through the plastochrone method. Using data on leaf biomass and length collected in three natural eelgrass populations and a mesocosm, we evaluated the suitability of a leaf weight-to-length ratio for nondestructive assessments. For the data sets considered, the isometric scaling that sustains the weight-to-length proxy always produced inconsistent fittings, and for leaf-lengths greater than a threshold value, the conversion of leaf length to biomass generated biased estimations. In contrast, an allometric scaling of leaf biomass and length was highly consistent in all the cases considered. And these nondestructive assessments generated reliable levels of reproducibility in leaf biomass for all the ranges of variability in leaf lengths. We argue that the use of allometric scaling for the representation of leaf biomass in terms of length provides a more reliable approach for estimating eelgrass biomass.

scholarly journals Effects of leaf length and development stage on the triple oxygen isotope signature of grass leaf water and phytoliths: insights for a proxy of continental atmospheric humidity

2019 ◽  
Vol 16 (23) ◽  
pp. 4613-4625 ◽  
Author(s):  
Anne Alexandre ◽  
Elizabeth Webb ◽  
Amaelle Landais ◽  
Clément Piel ◽  
Sébastien Devidal ◽  
...  
Keyword(s):  
Oxygen Isotope ◽  
Climate Model ◽  
Isotope Composition ◽  
Leaf Growth ◽  
Development Stage ◽  
Mean Value ◽  
Leaf Length ◽  
Leaf Water ◽  
Grass Species ◽  
Leaf Biomass

Abstract. Continental relative humidity (RH) is a key climate parameter, but there is a lack of quantitative RH proxies suitable for climate model–data comparisons. Recently, a combination of climate chamber and natural transect calibrations have laid the groundwork for examining the robustness of the triple oxygen isotope composition (δ′18O and 17O-excess) of phytoliths, that can preserve in sediments, as a new proxy for past changes in RH. However, it was recommended that besides RH, additional factors that may impact δ′18O and 17O-excess of plant water and phytoliths be examined. Here, the effects of grass leaf length, leaf development stage and day–night alternations are addressed from growth chamber experiments. The triple oxygen isotope compositions of leaf water and phytoliths of the grass species F. arundinacea are analysed. Evolution of the leaf water δ′18O and 17O-excess along the leaf length can be modelled using a string-of-lakes approach to which an unevaporated–evaporated mixing equation must be added. We show that for phytoliths to record this evolution, a kinetic fractionation between leaf water and silica, increasing from the base to the apex, must be assumed. Despite the isotope heterogeneity of leaf water along the leaf length, the bulk leaf phytolith δ′18O and 17O-excess values can be estimated from the Craig and Gordon model and a mean leaf water–phytolith fractionation exponent (λPhyto-LW) of 0.521. In addition to not being leaf length dependent, δ′18O and 17O-excess of grass phytoliths are expected to be impacted only very slightly by the stem vs. leaf biomass ratio. Our experiment additionally shows that because a lot of silica polymerises in grasses when the leaf reaches senescence (58 % of leaf phytoliths in mass), RH prevailing during the start of senescence should be considered in addition to RH prevailing during leaf growth when interpreting the 17O-excess of grass bulk phytoliths. Although under the study conditions 17O-excessPhyto do not vary significantly from constant day to day–night conditions, additional monitoring at low RH conditions should be done before drawing any generalisable conclusions. Overall, this study strengthens the reliability of the 17O-excess of phytoliths to be used as a proxy of RH. If future studies show that the mean value of 0.521 used for the grass leaf water–phytolith fractionation exponent λPhyto-LW is not climate dependent, then grassland leaf water 17O-excess obtained from grassland phytolith 17O-excess would inform on isotope signals of several soil–plant-atmosphere processes.

scholarly journals Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets

2017 ◽  
Vol 106 (11) ◽  
pp. 3270-3279 ◽  
Author(s):  
Maulik K. Nariya ◽  
Jae Hyun Kim ◽  
Jian Xiong ◽  
Peter A. Kleindl ◽  
Asha Hewarathna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Data Sets ◽  
Learning Approaches ◽  
Comparative Characterization ◽  
Analytical Stability ◽  
Stability Data ◽  
Using Data

scholarly journals Application of Direct Methods to Inorganic Materials Characterization

2014 ◽  
Vol 70 (a1) ◽  
pp. C29-C29
Author(s):  
Jordi Rius
Keyword(s):  
Fourier Transforms ◽  
Direct Methods ◽  
Inorganic Materials ◽  
Data Sets ◽  
Variable Degree ◽  
Patterson Function ◽  
Small Crystals ◽  
Structure Solution ◽  
Using Data

Although in the last years most attention has been paid to the development of direct methods (DM) in the macromolecular field, DM also play an important role in the characterization of inorganic materials. Very challenging is nowadays the structure solution of increasingly small crystals. Here the difficulty is not associated with the large number of atoms but with experimental limitations which may affect the data accuracy and the completeness of the data sets. It is obvious that DM have to adapt to this emerging scientific need. Particularly interesting has been the evolution of Patterson-function DM to cope with these objectives. The initial formulation based on the explicit use of triple-phase sums was modified to permit the calculation with Fourier transforms thus resulting in the more simple and accurate S-FFT algorithm [1]. Thanks to the resulting increased simplicity, this algorithm could be easily adapted to the treatment of powder diffraction data of complex inorganic materials [2]. The practical application of this algorithm is analyzed by using data of some synthetic and natural materials. Recently, the possibility of collecting good quality 3D intensity data from very small nanovolumes by new sophisticated electron diffraction (ED) techniques has become a reality. However, these data sets are often incomplete and, in addition, the intensities are not completely kinematical. The processing of these data sets represents a new challenge for DM. To this purpose a new (even more simple) Patterson-function DM (called delta-recycling) has been developed and tested on precession ED data from inorganic materials with variable degree of difficulty [3]. Phasing with delta-recycling proves to be highly efficient and from the interpretation of the results important practical conclusions can be drawn.

scholarly journals Mining SNPs From EST Databases

1999 ◽  
Vol 9 (2) ◽  
pp. 167-174 ◽  
Author(s):  
Leslie Picoult-Newberg ◽  
Trey E. Ideker ◽  
Mark G. Pohl ◽  
Scott L. Taylor ◽  
Miriam A. Donaldson ◽  
...  
Keyword(s):  
Expressed Sequence Tag ◽  
De Novo ◽  
Cdna Libraries ◽  
Human Populations ◽  
Data Sets ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Using Data

There is considerable interest in the discovery and characterization of single nucleotide polymorphisms (SNPs) to enable the analysis of the potential relationships between human genotype and phenotype. Here we present a strategy that permits the rapid discovery of SNPs from publicly available expressed sequence tag (EST) databases. From a set of ESTs derived from 19 different cDNA libraries, we assembled 300,000 distinct sequences and identified 850 mismatches from contiguous EST data sets (candidate SNP sites), without de novo sequencing. Through a polymerase-mediated, single-base, primer extension technique, Genetic Bit Analysis (GBA), we confirmed the presence of a subset of these candidate SNP sites and have estimated the allele frequencies in three human populations with different ethnic origins. Altogether, our approach provides a basis for rapid and efficient regional and genome-wide SNP discovery using data assembled from sequences from different libraries of cDNAs.[The SNPs identified in this study can be found in the National Center of Biotechnology (NCBI) SNP database under submitter handles ORCHID (SNPS-981210-A) and debnick (SNPS-981209-A and SNPS-981209-B).]

Electron Microscope Study of RNA's of Paramyxoviruses

1972 ◽  
Vol 30 ◽  
pp. 196-197
Author(s):  
Ruchama Baum ◽  
J.T. Seto
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Electron Microscope Study ◽  
Sendai Virus ◽  
Sodium Dodecyl ◽  
Rna Molecules ◽  
Virus Particles ◽  
Molecular Weights ◽  
Length Measurements

The ribonucleic acid (RNA) of paramyxoviruses has been characterized by biochemical and physiochemical methods. However, paramyxovirus RNA molecules have not been studied by electron microscopy. The molecular weights of these single-stranded viral RNA molecules are not known as yet. Since electron microscopy has been found to be useful for the characterization of single-stranded RNA, this investigation was initiated to examine the morphology and length measurements of paramyxovirus RNA's.Sendai virus Z strain and Newcastle disease virus (NDV), Milano strain, were used. For these studies it was necessary to develop a method of extracting RNA molecules from purified virus particles. Highly purified Sendai virus was treated with pronase (300 μg/ml) at 37°C for 30 minutes and the RNA extracted by the sodium dodecyl sulfate (SDS)-phenol procedure.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

scholarly journals Average fractional polarization of extragalactic sources at Planck frequencies

2018 ◽  
Vol 618 ◽  
pp. A29 ◽  
Author(s):  
T. Trombetti ◽  
C. Burigana ◽  
G. De Zotti ◽  
V. Galluzzi ◽  
M. Massardi
Keyword(s):  
High Sensitivity ◽  
Point Sources ◽  
Distribution Analysis ◽  
Microwave Background ◽  
Residual Noise ◽  
Upper Limits ◽  
Dusty Galaxies ◽  
Using Data ◽  
Polarization Fraction

Recent detailed simulations have shown that an insufficiently accurate characterization of the contamination of unresolved polarized extragalactic sources can seriously bias measurements of the primordial cosmic microwave background (CMB) power spectrum if the tensor-to-scalar ratio r ∼ 0.001, as predicted by models currently of special interest (e.g., Starobinsky’s R2 and Higgs inflation). This has motivated a reanalysis of the median polarization fraction of extragalactic sources (radio-loud AGNs and dusty galaxies) using data from the Planck polarization maps. Our approach, exploiting the intensity distribution analysis, mitigates or overcomes the most delicate aspects of earlier analyses based on stacking techniques. By means of simulations, we have shown that the residual noise bias on the median polarization fraction, Πmedian, of extragalactic sources is generally ≲0.1%. For radio sources, we have found Πmedian ≃ 2.83%, with no significant dependence on either frequency or flux density, in good agreement with the earlier estimate and with high-sensitivity measurements in the frequency range 5–40 GHz. No polarization signal is detected in the case of dusty galaxies, implying 90% confidence upper limits of Πdusty ≲ 2.2% at 353 GHz and of ≲3.9% at 217 GHz. The contamination of CMB polarization maps by unresolved point sources is discussed.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Model-Informed Optimization of a Pediatric Clinical Pharmacokinetic Trial of a New Spironolactone Liquid Formulation

Pharmaceutics ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 849
Author(s):  
Manasa Tatipalli ◽  
Vijay Kumar Siripuram ◽  
Tao Long ◽  
Diana Shuster ◽  
Galina Bernstein ◽  
...  
Keyword(s):  
Pharmacokinetic Model ◽  
Allometric Scaling ◽  
Pediatric Patients ◽  
Reference Product ◽  
Population Pharmacokinetic ◽  
Liquid Formulation ◽  
Sampling Schemes ◽  
Quantitative Pharmacology ◽  
Using Data ◽  
Drug Pharmacokinetics

Quantitative pharmacology brings important advantages in the design and conduct of pediatric clinical trials. Herein, we demonstrate the application of a model-based approach to select doses and pharmacokinetic sampling scenarios for the clinical evaluation of a novel oral suspension of spironolactone in pediatric patients with edema. A population pharmacokinetic model was developed and qualified for spironolactone and its metabolite, canrenone, using data from adults and bridged to pediatrics (2 to <17 years old) using allometric scaling. The model was then used via simulation to explore different dosing and sampling scenarios. Doses of 0.5 and 1.5 mg/kg led to target exposures (i.e., similar to 25 and 100 mg of the reference product in adults) in all the reference pediatric ages (i.e., 2, 6, 12 and 17 years). Additionally, two different sampling scenarios were delineated to accommodate patients into sparse sampling schemes informative to characterize drug pharmacokinetics while minimizing phlebotomy and burden to participating children.

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