Recent pollution and source identification of metal(loid)s in a sediment core from Gunsan Reservoir, South Korea.

We assessed the contributions of Cu smelting and Asian dust influx to metal(loid) pollution over the last century based on metal(loid) concentrations, Pb isotope ratios, and 210Pb dating of sediments from Gunsan Reservoir, South Korea. During the pre-industrial period (1827-1936), heavy metal pollutants were predominantly geogenic. Local smelting activity was dominant between 1936 and 2008. Between 1936 and 1971, Cu and Pb contamination was not significant, with contributions from smelting activities accounting for < 6.2% of the measured concentrations. In contrast, anthropogenic Cu and Pb inputs increased between 1971 and 2008 due to intense smelting activity. Based on the similar 206Pb/207Pb ratios of the reservoir sediments and smelter bottom ash, anthropogenic contributions reached 95% during 1987-1995. In comparison, the sediments deposited between 2008 and 2018 were characterized by a higher Zn and Cd geoaccumulation index and enrichment factor values and less-radiogenic Pb signals than the regional geogenic Pb signal. These trends are attributed to the long-range transport of pollutants from China, the contribution of which gradually increased from 11.0% in 2008 to 19.2% in 2018. Our results show that reservoir sediments are valuable archives for reconstructing metal(loid) contamination histories and changes in pollution sources over time.

Asynchronous multi-decadal time-scale series of biotic and abiotic responses to precipitation during the last 1300 years.

East Asian summer monsoon (EASM)-driven rapid hydroclimatic variation is a crucial factor with major socioeconomic impacts. Nevertheless, decadal- to centennial-scale EASM variability over the last two millennia is still poorly understood. Pollen-based quantitative annual precipitation (PqPann) and annual precipitation reconstructed by artificial neural networks (ANNs) for the period 650-1940 CE were reconstructed from a paleo-reservoir in South Korea. ANNs reconstruction was performed to compensate for a hiatus section. On a decadal timescale, 10 high-precipitation periods were identified, and PqPann and ANNs reconstructions were comparable to local instrumental rainfall and historic drought records. Biotic lags to rapid climatic changes ranging from 25 to 100 years were recognized by asynchronous pollen and speleothem responses to precipitation. We suggest that PqPann-based decadal- to centennial-scale climatic change reconstruction should take biotic lags into account, although the lags can be ignored on the millennial scale. The position of the EASM rainband influenced rainfall magnitude.

Characterization of the contribution of road deposited sediments to the contamination of the close marine environment with trace metals: Case of the port city of Busan (South Korea).

We examined the concentrations of 12 trace metals in road-deposited sediments (RDS) and their contributions to the pollution of harbor/marine sediments in the port city of Busan, South Korea. The concentrations of Cr, Cu, Zn, Cd, Sn, Sb, and Pb in RDS affected by industrial and traffic activities were 6.7-25.7 times higher than those in marine sediments. The enrichment factors indicate that RDS are extremely polluted with Sb and moderately to strongly polluted with Cr, Cu, As, Sn, Pb, Zn, and Cd. The mean modified pollution index distinguished between unpolluted marine sediments, moderately to heavily polluted harbor sediments, and severely polluted RDS. Furthermore, harbor/marine sediments close to shipyards and the mouths of streams and rivers were found to be moderately polluted with Cu, Zn, Cd, Sb, and Pb. RDS containing trace metals accumulate on impervious surfaces and flow into the marine environment via untreated stormwater runoff.

Heavy metal pollution by road-deposited sediments and its contribution to total suspended solids in rainfall runoff from intensive industrial areas.

Understanding the relationship between road-deposited sediments (RDS) and total suspended solids (TSS) is essential for managing non-point pollution. Studying the heavy metal concentrations of RDS and TSS in rainfall is important to the development of RDS management strategies and to the design of effective stormwater management practices. We investigated the heavy metal (V, Cr, Mn, Co, Ni, Cu, Zn, As, Mo, Cd, Sn, Pb) in RDS and TSS in rainfall runoff to assess the metal pollution level and to evaluate the contribution of RDS heavy metal pollution in the TSS. The heavy metal pollution in RDS and TSS in industrial areas was relatively higher in small particles (<125 µm), while TSS had a higher heavy metal concentration than RDS. In addition, the concentration of heavy metals in TSS increased rapidly during the initial rainfall. The amount of particles larger than 125 µm also increased, suggesting that large metal particles accumulated in industrial areas were also discharged in the form of non-point pollution. The amount of RDS per unit of industrial area (g/m2) and the accumulation of heavy metals (Pb, Cu, and Zn) (mg/m2) were 15- and 8-54-fold higher than those of urban areas, respectively. Considering particles <125 µm, which can be easily transported or discharged during rainfall, the contribution rate of RDS to TSS was 41.3%, but the average contribution rate to heavy metals in TSS was 22.1%. The average load of heavy metals from industrial areas in TSS was 77.9%. The load of Cu, Ni, As, Cd, and Sn exceeded 90%, indicating that most of these metals were attributed to industrial activities related to metal processing. Our results suggest the importance of efficient road cleaning and rainfall runoff management strategies to solve the heavy metal pollution problem caused by non-point sources in industrial areas.

Input of terrestrial organic matter linked to deglaciation increased mercury transport to the Svalbard fjords.

Deglaciation has accelerated the transport of minerals as well as modern and ancient organic matter from land to fjord sediments in Spitsbergen, Svalbard, in the European Arctic Ocean. Consequently, such sediments may contain significant levels of total mercury (THg) bound to terrestrial organic matter. The present study compared THg contents in surface sediments from three fjord settings in Spitsbergen: Hornsund in the southern Spitsbergen, which has high annual volume of loss glacier and receives sediment from multiple tidewater glaciers, Dicksonfjorden in the central Spitsbergen, which receives sediment from glacifluvial rivers, and Wijdefjorden in the northern Spitsbergen, which receive sediments from a mixture of tidewater glaciers and glacifluvial rivers. Our results showed that the THg (52 ± 15 ng g-1) bound to organic matter (OM) was the highest in the Hornsund surface sediments, where the glacier loss (0.44 km3 yr-1) and organic carbon accumulation rates (9.3 ~ 49.4 g m-2 yr-1) were elevated compared to other fjords. Furthermore, the δ13C (-27 ~ -24‰) and δ34S values (-10 ~ 15‰) of OM indicated that most of OM were originated from terrestrial sources. Thus, the temperature-driven glacial melting could release more OM originating from the meltwater or terrestrial materials, which are available for THg binding in the European Arctic fjord ecosystems.

Normalization of gene/protein names in biological literatures using Vector-Space Model.

As the number of biological literatures grows exponentially, needs for text mining system are increased. In text mining area, normalization is mapping gene/protein names to a database. It is necessary to combine extracted information from various literatures and to create a database or an ontology using literatures. Previous normalization researches used direct comparison methods between a database and literatures, but it is weak to extremely variational gene/protein names in literatures. Therefore, in this paper, we propose a normalization method using Vector-Space Model. For each gene/protein name, we rank identifiers using Vector-Space Model, and find the most similar identifier with the name. Experimental result shows the proposed method has 70.7% f-measure.

Finding the evidence for protein-protein interactions from PubMed abstracts.

MOTIVATION: Protein-protein interactions play critical roles in biological processes, and many biologists try to find or to predict crucial information concerning these interactions. Before verifying interactions in biological laboratory work, validating them from previous research is necessary. Although many efforts have been made to create databases that store verified information in a structured form, much interaction information still remains as unstructured text. As the amount of new publications has increased rapidly, a large amount of research has sought to extract interactions from the text automatically. However, there remain various difficulties associated with the process of applying automatically generated results into manually annotated databases. For interactions that are not found in manually stored databases, researchers attempt to search for abstracts or full papers. RESULTS: As a result of a search for two proteins, PubMed frequently returns hundreds of abstracts. In this paper, a method is introduced that validates protein-protein interactions from PubMed abstracts. A query is generated from two given proteins automatically and abstracts are then collected from PubMed. Following this, target proteins and their synonyms are recognized and their interaction information is extracted from the collection. It was found that 67.37% of the interactions from DIP-PPI corpus were found from the PubMed abstracts and 87.37% of interactions were found from the given full texts. AVAILABILITY: Contact authors.

BioProber: software system for biomedical relation discovery from PubMed.

The numbers of articles and journals that are published are increasing at a considerable rate, and the published information is growing continuously and fast. Because of this, researches to acquire knowledge automatically have been carried out in the areas of information retrieval, information extraction and text mining. Information retrieval approaches are good for specific topics that the number of related articles is small. But, if the number is bigger, searching skill and knowledge acquisition ability are useless. Though many efforts have been made to extract information from literature, many approaches have concentrated on specific entities, such as proteins, genes and their interactions, and much information is still remained in unstructured text. So, we have developed a system that discovers relations between various categories of biomedical entities. Our system collects abstracts from PubMed by queries representing a topic and visualizes relationship from the collection by automatic information extraction.