Dataset Information

Rapid assessment of iron in blood plasma and serum by spectrophotometry with cloud-point extraction.

ABSTRACT: Rapid photometric assessment of iron in blood plasma and serum by a simple procedure after the extraction of iron(II) complex with 1-nitroso-2-naphthol in the micellar phase of a nonionic surfactant at the cloud point upon heating (pH range is 4.5-6.3) is proposed. The procedure trueness was verified using a standard reference protocol using bathophenanthroline. The advantages of the procedure are higher sensitivity than the reference protocol: the limit of detection is 0.03 ?g/mL, the limit of quantitation is 0.1 ?g/mL, the determination range is 0.1 - 2.8 ?g/mL (RSD 0.02-0.10). Copper does not interfere with the iron assessment.

SUBMITTER: Samarina T

PROVIDER: S-EPMC4863669 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Rapid assessment of iron in blood plasma and serum by spectrophotometry with cloud-point extraction.

Samarina Tatyana T Proskurnin Mikhail M

F1000Research 20150825

Rapid photometric assessment of iron in blood plasma and serum by a simple procedure after the extraction of iron(II) complex with 1-nitroso-2-naphthol in the micellar phase of a nonionic surfactant at the cloud point upon heating (pH range is 4.5-6.3) is proposed. The procedure trueness was verified using a standard reference protocol using bathophenanthroline. The advantages of the procedure are higher sensitivity than the reference protocol: the limit of detection is 0.03 μg/mL, the limit of ...[more]

PMID: 27239269

Similar Datasets

Project description:ReasonPedestrian recognition has great practical value and is a vital step toward applying path planning and intelligent obstacle avoidance in autonomous driving. In recent years, laser radar has played an essential role in pedestrian detection and recognition in unmanned driving. More accurate high spatial dimension and high-resolution data could be obtained by building a three-dimensional point cloud. However, the point cloud data collected by laser radar is often massive and contains a lot of redundancy, which is not conducive to transmission and storage. So, the processing speed grows slow when the original point cloud data is used for recognition. On the other hand, the compression processing of many laser radar point clouds could save computing power and speed up the recognition processing.MethodologyThe article utilizes the fusion point cloud data from laser radar to investigate the fast pedestrian recognition algorithm. The focus is to compress the collected point cloud data based on the boundary and feature value extraction and then use the point cloud pedestrian recognition algorithm based on image mapping to detect pedestrians. This article proposes a point cloud data compression method based on feature point extraction and reduced voxel grid.ResultsThe Karlsruhe Institute of Technology and Toyota Technological Institute data set is used to investigate the proposed algorithm experimentally. The outcomes indicate that the peak signal-to-noise ratio of the compression algorithm is improved by 6.02%. The recognition accuracy is improved by 16.93%, 17.2%, and 16.12%, corresponding to simple, medium, and difficult scenes, respectively, when compared with the point cloud pedestrian recognition method based on image mapping, which uses the random sampling method to compress the point cloud data.ConclusionThe proposed method could achieve data compression better and ensure that many feature points are retained in the compressed Point Cloud Data (PCD). Thus, the compressed PCD achieves pedestrian recognition through an image-based mapping recognition algorithm.

Project description:Fish morphological phenotypes are important resources in artificial breeding, functional gene mapping, and population-based studies in aquaculture and ecology. Traditional morphological measurement of phenotypes is rather expensive in terms of time and labor. More importantly, manual measurement is highly dependent on operational experience, which can lead to subjective phenotyping results. Here, we developed 3DPhenoFish software to extract fish morphological phenotypes from three-dimensional (3D) point cloud data. Algorithms for background elimination, coordinate normalization, image segmentation, key point recognition, and phenotype extraction were developed and integrated into an intuitive user interface. Furthermore, 18 key points and traditional 2D morphological traits, along with 3D phenotypes, including area and volume, can be automatically obtained in a visualized manner. Intuitive fine-tuning of key points and customized definitions of phenotypes are also allowed in the software. Using 3DPhenoFish, we performed high-throughput phenotyping for four endemic Schizothoracinae species, including Schizopygopsis younghusbandi, Oxygymnocypris stewartii, Ptychobarbus dipogon, and Schizothorax oconnori. Results indicated that the morphological phenotypes from 3DPhenoFish exhibited high linear correlation (>0.94) with manual measurements and offered informative traits to discriminate samples of different species and even for different populations of the same species. In summary, we developed an efficient, accurate, and customizable tool, 3DPhenoFish, to extract morphological phenotypes from point cloud data, which should help overcome traditional challenges in manual measurements. 3DPhenoFish can be used for research on morphological phenotypes in fish, including functional gene mapping, artificial selection, and conservation studies. 3DPhenoFish is an open-source software and can be downloaded for free at https://github.com/lyh24k/3DPhenoFish/tree/master.

Dataset Information

Rapid assessment of iron in blood plasma and serum by spectrophotometry with cloud-point extraction.

Publications

Rapid assessment of iron in blood plasma and serum by spectrophotometry with cloud-point extraction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets