Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature

Bio-entity name recognition is the key step for information extraction from biomedical literature. This paper presents a dictionary-based bio-entity name recognition approach. The approach expands the bio-entity name dictionary via the Abbreviation Definitions identifying algorithm, improves the recall rate through the improved edit distance algorithm and adopts some post-processing methods including Pre-keyword and Post-keyword expansion, Part of Speech expansion, merge of adjacent bio-entity names and the exploitation of the contextual cues to further improve the performance. Experiment results show that with this approach even an internal dictionary-based system could achieve a fairly good performance.     

Contents of heavy metals in lotus seed from REEs mining area

At rare earth mining area, the pollution of heavy metals has been paid little attention. In lotus seeds and soil samples sampled from a typical rare earth elements mining area were detected the contents of eight kinds of heavy metals (Cr, Mn, Cu, Zn, As, Cd, Hg and Pb) to investigate the pollution from rare earth elements mining by ICP-MS (Inductively Coupled Plasma Mass Spectrometry). The results showed that the contents of the heavy metals Cr, Mn, Cu, Zn, As, Cd, Hg and Pb in lotus seeds were 0.25, 86.94, 8.32, 19.76, 0.13, 0.08, 0.01, and 0.20 μg/g in lotus seeds, respectively, and corresponding contents in soil were 27.68, 168.71, 20.23, 47.16, 1.83, 0.13, 0.04, and 23.15 μg/g. These data showed that the contents of heavy metals in lotus seeds from REEs mining area are no higher than reference area and meet national food safety standards of China.     

“对比实验+现象驱动的知识挖掘”在高分子材料合成创新实验教学中的尝试与运用

高分子材料合成实验是高分子专业的核心实验课程之一,如何建立实验课程与理论课程的有效衔接,加深学生对所学知识点的深入理解和活学活用,是本课程的主要目标。为此,笔者和教学团队提出：在同一个实验中改变某个实验变量,在教学班中引入对比实验,通过这些改变引起的不同实验现象和由此产生的材料性能的迥然差异,引导学生从现象出发,联系理论课堂知识进行深入分析。这种尝试有效改变了以往实验教学中所有教学班千篇一律的操作和几乎完全相同的实验现象与结果,有效激发了学生的主动性,提高了其分析问题、解决问题的能力。2年的实验教学反馈表明,“对比实验的引入”和“现象驱动的知识挖掘”帮助学生有效巩固了理论课堂的知识点,做到了活学活用。     

ToxML,a data exchange standard with content controlled vocabulary used to build better (Q)SAR models

Development of accurate quantitative structure–activity relationship (QSAR) models requires the availability of high quality validated data. International regulations such as REACH in Europe will now accept (Q)SAR-based evaluations for risk assessment. The number of toxicity datasets available for those wishing to share knowledge, or to use for data mining and modelling, is continually expanding. The challenge is the current use of a multitude of different data formats. The issues of comparing or combining disparate data apply both to public and proprietary sources. The ToxML project addresses the need for a common data exchange standard that allows the representation and communication of these data in a well-structured electronic format. It is an open standard based on Extensible Markup Language (XML). Supporting information for overall toxicity endpoint data can be included within ToxML files. This makes it possible to assess the quality and detail of the data used in a model. The data file model allows the aggregation of experimental data to the compound level in the detail needed to support (Q)SAR work. The standard is published on a website together with tools to view, edit and download it.     

An Analytical Method for the Separation and Determination of As(III) and As(V) in Seepage and Acid Mine Drainage Water

To avoid changes in the original As species distribution in natural water after sampling, a method of immediate separation of As(V) by anion exchange at the sampling site was developed. The procedure consists of two steps. The total concentration of arsenic is determined in one part of the water sample acidified on site. Another part of the water samples is pressed through a column filled with an anion exchanger. The As(III) species that is not redox-stable remains in the effluent of the sorbents column and can be analyzed with conventional methods after stabilization by addition of conc. HNO₃. As(V) is sorbed by the exchanger material. The As(V) concentration can be calculated as the difference between As_sol and As(III), neglecting very low contents of methylated species. Oxidation of Fe(II) by air followed by co-precipitation of arsenic with iron hydroxide was applied in field experiments to minimize the As concentration in seepage and mining water.     

Comparison of two algorithmic data processing strategies for metabolic fingerprinting by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

The alignment algorithm Statistical Compare (SC) developed by LECO Corporation for the processing of comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) data was validated and compared to the in-house developed retention time correction and data alignment tool INCA (Integrative Normalization and Comparative Analysis) by a spike-in experiment and the comparative metabolic fingerprinting of a wild type versus a double mutant strain of Escherichia coli (E. coli). Starting with the same peak lists generated by LECO's ChromaTOF software, the accuracy of peak alignment and detection of 1.1- to 4-fold changes in metabolite concentration was assessed by spiking 20 standard compounds into an aqueous methanol extract of E. coli. To provide the same quality input signals for both alignment routines, the universal m/z 73 trace of the trimethylsilyl (TMS) group was used as a quantitative measure for all features. The performance of data processing and alignment was evaluated and illustrated by ROC curves. Statistical Compare performed marginally better at the lower fold changes, while INCA did so at the higher fold changes. Using SC, quantitative precision could be improved substantially by exploiting the signal intensities of metabolite-specific unique (U) m/z ion traces rather than the universal m/z 73 trace. A list of 56 features that distinguished the two E. coli strains was obtained by the SC alignment using m/z U with an estimated false discovery rate (FDR) of <0.05. Ultimately, 23 metabolites could be identified, one additional and five less than with INCA due to the failure of SC to extract unitized m/z U's across all fingerprints with suitable spectral intensities for the latter metabolites.     

Phosphate recognition in structural biology

Drug-discovery research in the past decade has seen an increased selection of targets with phosphate recognition sites, such as protein kinases and phosphatases, in the past decade. This review attempts, with the help of database-mining tools, to give an overview of the most important principles in molecular recognition of phosphate groups by enzymes. A total of 3003 X-ray crystal structures from the RCSB Protein Data Bank with bound organophosphates has been analyzed individually, in particular for H-bonding interactions between proteins and ligands. The various known binding motifs for phosphate binding are reviewed, and similarities to phosphate complexation by synthetic receptors are highlighted. An analysis of the propensities of amino acids in various classes of phosphate-binding enzymes showed characteristic distributions of amino acids used for phosphate binding. This review demonstrates that structure-based lead development and optimization should carefully address the phosphate-binding-site environment and also proposes new alternatives for filling such sites.     

Urinary exposure marker discovery for toxicants using ultra-high pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry and three untargeted metabolomics approaches

Human biomonitoring is the assessment of actual internal contamination of chemicals by measuring exposure markers, chemicals or their metabolites, in human urine, blood, serum, and other body fluids. However, the metabolism of chemicals within an organism is extremely complex. Therefore, the identification of metabolites is often difficult and laborious. Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification. In this study, three metabolomics data processing approaches were used for chemical exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset; di-isononyl phthalate (DINP) was used as an example. The data processing techniques included the SMAIT, mass defect filtering (MDF), and XCMS Online. Sixteen, 83, and 139 probable DINP metabolite signals were obtained using the SMAIT, MDF, and XCMS procedures, respectively. Fourteen probable metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS. Among them, 13 probable metabolite signals were validated as exposure-related markers in a rat model. Six (m/z 319.155, 361.127, 373.126, 389.157, 437.112 and 443.130) of the 13 exposure-related DINP metabolite signals have not previously been reported in the literature. Our data indicate that SMAIT provided an efficient method to discover effectively and systematically urinary exposure markers of toxicant. The DINP metabolism information can provide valuable information for further investigations of DINP toxicity, toxicokinetics, exposure assessment, and human health effects.