Dataset Information

Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition

ABSTRACT: In data independent acquisition, consistent, comprehensive MS1 and MS2 can be recorded. This enables statistical comparison based on the two quantitative levels.

INSTRUMENT(S): Q Exactive HF-X, Q Exactive HF

ORGANISM(S): Arabidopsis Thaliana (ncbitaxon:3702) Escherichia Coli (ncbitaxon:562) Saccharomyces Cerevisiae (ncbitaxon:4932) Homo Sapiens (ncbitaxon:9606) Mus Musculus (ncbitaxon:10090) Caenorhabditis Elegans (ncbitaxon:6239)

SUBMITTER: Lukas Reiter

PROVIDER: MSV000084864 | MassIVE | Thu Jan 30 03:07:00 GMT 2020

SECONDARY ACCESSION(S): PXD016647

REPOSITORIES: MassIVE

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Publications

Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition.

Huang Ting T Bruderer Roland R Muntel Jan J Xuan Yue Y Vitek Olga O Reiter Lukas L

Molecular & cellular proteomics : MCP 20191230 2

In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often ...[more]

PMID: 31888964

Similar Datasets

Project description:BackgroundOne of the key elements in maintaining the consistent marketing of tomato fruit is tomato quality. Since ripeness is the most important factor for tomato quality in the viewpoint of consumers, determining the stages of tomato ripeness is a fundamental industrial concern with regard to tomato production to obtain a high quality product. Since tomatoes are one of the most important crops in the world, automatic ripeness evaluation of tomatoes is a significant study topic as it may prove beneficial in ensuring an optimal production of high-quality product, increasing profitability. This article explores and categorises the various maturity/ripeness phases to propose an automated multi-class classification approach for tomato ripeness testing and evaluation.MethodsObject detection is the critical component in a wide variety of computer vision problems and applications such as manufacturing, agriculture, medicine, and autonomous driving. Due to the tomato fruits' complex identification background, texture disruption, and partial occlusion, the classic deep learning object detection approach (YOLO) has a poor rate of success in detecting tomato fruits. To figure out these issues, this article proposes an improved YOLOv5 tomato detection algorithm. The proposed algorithm CAM-YOLO uses YOLOv5 for feature extraction, target identification and Convolutional Block Attention Module (CBAM). The CBAM is added to the CAM-YOLO to focus the model on improving accuracy. Finally, non-maximum suppression and distance intersection over union (DIoU) are applied to enhance the identification of overlapping objects in the image.ResultsSeveral images from the dataset were chosen for testing to assess the model's performance, and the detection performance of the CAM-YOLO and standard YOLOv5 models under various conditions was compared. The experimental results affirms that CAM-YOLO algorithm is efficient in detecting the overlapped and small tomatoes with an average precision of 88.1%.