Spatial and temporal variations of net ecosystem productivity in Xinjiang Autonomous Region, China based on remote sensing.

Net ecosystem productivity (NEP), which plays a key role in the carbon cycle, is an important indicator of the ecosystem's carbon budget. In this paper, the spatial and temporal variations of NEP over Xinjiang Autonomous Region, China from 2001 to 2020 were studied based on remote sensing and climate re-analysis data. The modified Carnegie Ames Stanford Approach (CASA) model was employed to estimate net primary productivity (NPP), and the soil heterotrophic respiration model was used to calculate soil heterotrophic respiration. Then NEP was obtained by calculating the difference between NPP and heterotrophic respiration. The annual mean NEP of the study area was high in the east and low in the west, high in the north and low in the south. The 20-year mean vegetation NEP of the study area is 128.54 gC·m-2, indicating that the study area is a carbon sink on the whole. From 2001 to 2020, the annual mean vegetation NEP ranged between 93.12 and 158.05 gC·m-2, and exhibited an increasing trend in general. 71.46% of the vegetation area showed increasing trends of NEP. NEP exhibited a positive relationship with precipitation and a negative relationship with air temperature, and the correlation with air temperature was more significant. The work reveals the spatio-temporal dynamics of NEP in Xinjiang Autonomous Region and can provide a valuable reference for assessing regional carbon sequestration capacity.

Exploring the formation mechanism of fine particles in an ex-heavily polluted Northwestern city, China.

Fine particle pollution is still a severe issue in the northwestern region of China where the formation mechanism of which remains ambiguous due to the limited studies there. In this study, a comprehensive study on the chemical composition and sources of PM2.5 at an ex-heavily polluted northwestern city was conducted, based on filter sampling data obtained from three consecutive winter campaigns during 2020-2022. The average PM2.5 during the three winter campaigns were 170.9 ± 66.4, 249.0 ± 75.7, and 200.9 ± 47.6 µg/m3, respectively, with the daily maximum value of PM2.5 exceeds 400 µg/m3 under stagnant meteorological conditions charactered by high relative humidity (>60 %) and low wind speed (<1 m/s). The major chemical components in PM2.5 were found to be inorganic aerosol (55.2 %) that mainly constituted by sulfate (24.2 %), and mineral dust (14.9 %); while the carbonous species contributed a minor fraction (∼13 %). In addition, (NH4)2SO4 and NH4NO3 were the dominate contributors to appearance of low visibility (<3 km) which together accounting for over 85 % of light extinction coefficient (bext) during heavy polluted period. Source appointment of fine particles was then conducted by applying the positive matrix factorization method, and the primary sources were resolved to be coal combustion (27.7 %) and biomass burning (18.6 %), followed by industrial dust (16.2 %), residential combustion (15.3 %), traffic emissions (11.9 %) and dust aerosol (10.4 %). To explore the potential formation mechanism of fine particle pollution, the chemical evolution pattern combined with gaseous pollutants and meteorological parameters were further analyzed, which refine the important role of primary emissions in the forming of high sulfate aerosol loading, while secondary formation was largely suppressed during the winter period that totally different from those reported in the developed regions of China, thus indicating more effort should be paid on the reduction of primary particles emissions in the northwestern cities than on its gaseous percussors.

Mining relational paths in integrated biomedical data.

Much life science and biology research requires an understanding of complex relationships between biological entities (genes, compounds, pathways, diseases, and so on). There is a wealth of data on such relationships in publicly available datasets and publications, but these sources are overlapped and distributed so that finding pertinent relational data is increasingly difficult. Whilst most public datasets have associated tools for searching, there is a lack of searching methods that can cross data sources and that in particular search not only based on the biological entities themselves but also on the relationships between them. In this paper, we demonstrate how graph-theoretic algorithms for mining relational paths can be used together with a previous integrative data resource we developed called Chem2Bio2RDF to extract new biological insights about the relationships between such entities. In particular, we use these methods to investigate the genetic basis of side-effects of thiazolinedione drugs, and in particular make a hypothesis for the recently discovered cardiac side-effects of Rosiglitazone (Avandia) and a prediction for Pioglitazone which is backed up by recent clinical studies.

Semantic inference using chemogenomics data for drug discovery.

BACKGROUND: Semantic Web Technology (SWT) makes it possible to integrate and search the large volume of life science datasets in the public domain, as demonstrated by well-known linked data projects such as LODD, Bio2RDF, and Chem2Bio2RDF. Integration of these sets creates large networks of information. We have previously described a tool called WENDI for aggregating information pertaining to new chemical compounds, effectively creating evidence paths relating the compounds to genes, diseases and so on. In this paper we examine the utility of automatically inferring new compound-disease associations (and thus new links in the network) based on semantically marked-up versions of these evidence paths, rule-sets and inference engines. RESULTS: Through the implementation of a semantic inference algorithm, rule set, Semantic Web methods (RDF, OWL and SPARQL) and new interfaces, we have created a new tool called Chemogenomic Explorer that uses networks of ontologically annotated RDF statements along with deductive reasoning tools to infer new associations between the query structure and genes and diseases from WENDI results. The tool then permits interactive clustering and filtering of these evidence paths. CONCLUSIONS: We present a new aggregate approach to inferring links between chemical compounds and diseases using semantic inference. This approach allows multiple evidence paths between compounds and diseases to be identified using a rule-set and semantically annotated data, and for these evidence paths to be clustered to show overall evidence linking the compound to a disease. We believe this is a powerful approach, because it allows compound-disease relationships to be ranked by the amount of evidence supporting them.