Since the ancient discovery of the 'sweet odor' in human breath gas, pursuits of the breath analysis-based disease diagnostics have never stopped. Actually, the 'smell' of the breath, as one of three key disease diagnostic techniques, has been used in Eastern-Medicine for more than three thousand years. With advancement of measuring technologies in sensitivity and selectivity, more specific breath gas species have been identified and established as a biomarker of a particular disease. Acetone is one of the breath gases and its concentration in exhaled breath can now be determined with high accuracy using various techniques and methods. With the worldwide prevalence of diabetes that is typically diagnosed through blood testing, human desire to achieve non-blood based diabetic diagnostics and monitoring has never been quenched. Questions, such as is breath acetone a biomarker of diabetes and how is the breath acetone related to the blood glucose (BG) level (the golden criterion currently used in clinic for diabetes diagnostic, monitoring, and management), remain to be answered. A majority of current research efforts in breath acetone measurements and its technology developments focus on addressing the first question. The effort to tackle the second question has begun recently. The earliest breath acetone measurement in clearly defined diabetic patients was reported more than 60 years ago. For more than a half-century, as reviewed in this paper, there have been more than 41 independent studies of breath acetone using various techniques and methods, and more than 3211 human subjects, including 1581 healthy people, 242 Type 1 diabetic patients, 384 Type 2 diabetic patients, 174 unspecified diabetic patients, and 830 non-diabetic patients or healthy subjects who are under various physiological conditions, have been used in the studies. The results of the breath acetone measurements collected in this review support that many conditions might cause changes to breath acetone concentrations; however, the results from the six independent studies using clearly-defined Type 1 and Type 2 diabetic patients unanimously support that an elevated mean breath acetone concentration exists in Type 1 diabetes. Note that there is some overlap between the ranges of breath acetone concentration in individual T1D patients and healthy subjects; this reminds one to be careful when using an acetone breath test on T1D diagnostics. Comparatively, it is too early to draw a general conclusion on the relationship between a breath acetone level and a BG level from the very limited data in the literature.
The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.
The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.
The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn.
Element concentration within a plant which is vital to function maintenance and adaptation to environment, may change with plant growth. However, how carbon (C), nitrogen (N), and phosphorus (P) vary stoichiometrically with stand growth, i.e., ages or cuts, was still untouched in perennial species. This study tested the hypothesis that lucerne (Medicago sativa) C:N, C:P, and N:P should change with stand age and cut. Leaf C:N, C:P, and N:P changed with stand age, showing various trends in different cuts of lucerne. Generally the greatest stoichiometric ratios were measured in 8 year stand and in the second cut. They were affected significantly and negatively by total N and P concentrations of leaf, but not by organic C concentration. There were significantly positive correlations among leaf C:N, C:P, and N:P. However, leaf C:N, C:P, and N:P were hardly affected by soil features. Conclusively, lucerne C, N, and P stoichiometry are age- and cut-specific, and regulated mainly by leaf N, P concentrations and stoichiometry. There are few correlations with soil fertility. To our knowledge, it is the first try to elucidate the stoichiometry in the viewpoint of age and cut with a perennial herbaceous legume.
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