Phosphorus retention in lab and field-scale subsurface-flow wetlands treating plant nursery runoff

Constructed wetland systems built to handle nutrient contaminants are often efficient at removing nitrogen, but ineffective at reducing phosphorus (P) loads. Incorporating a clay-based substrate can enhance P removal in subsurface-flow constructed wetland systems. We evaluated the potential of crushed brick, a recycled building product, and two particle sizes of a palygorskite–bentonite industrial mineral aggregate (calcined clay) to sorb P from simulated nutrient-rich plant nursery effluent. The three substrates were screened for P sorbing behavior using sorption, desorption, and equilibration experiments. We selected one substrate to evaluate in an 8-month field trial to compare field sorption capacity with laboratory sorption capacity. In the laboratory, coarse calcined clay average sorption capacity was 497 mg kg⁻¹ and it sorbed the highest percentage of P supplied (76%), except at exposure concentrations >100 mg L⁻¹ where the increased surface area of fine calcined clay augmented its P sorption capacity. Subsurface-flow mesocosms were filled with coarse calcined clay and exposed to a four and seven day hydraulic retention time treatment. Phosphorus export was reduced by 60 to 74% for both treatments until substrate P-binding sites began to saturate during month seven. During the eight month experiment, the four and seven day treatments fixed 1273 ± 22 mg kg⁻¹ P and 937 ± 16 mg kg⁻¹ P, respectively. Sequential extractions of the P saturated clay indicated that P could desorb slowly over time from various pools within the calcined clay; thus, if the calcined clay were recycled as a soil amendment, most P released would be slowly available for plant uptake and use. This study demonstrated the viability of using coarse calcined clay as a root bed substrate in subsurface-flow treatment wetlands remediating phosphorus from plant nursery runoff.     

Acid-sensing ion channel 3 mediates peripheral anti-hyperalgesia effects of acupuncture in mice inflammatory pain

Background  

Peripheral tissue inflammation initiates hyperalgesia accompanied by tissue acidosis, nociceptor activation, and inflammation mediators. Recent studies have suggested a significantly increased expression of acid-sensing ion channel 3 (ASIC3) in both carrageenan- and complete Freund's adjuvant (CFA)-induced inflammation. This study tested the hypothesis that acupuncture is curative for mechanical hyperalgesia induced by peripheral inflammation.     

Application of liquid chromatography-mass spectrometry to the investigation of poisoning by Oenanthe crocata

Liquid chromatography-mass spectrometry (LC-MS) analysis of methanol extracts of Oenanthe crocata roots revealed that oenanthotoxin co-eluted with another major polyalkyne, 2,3-dihydro-oenanthotoxin, using the existing high performance liquid chromatography (HPLC) method (isocratic elution from C18 with aqueous methanol) for investigating Oenanthe poisoning. Positive ES or APCI gave [(M+H)-H(2)O](+) and its methanol adduct as major ion species for oenanthotoxin, whereas 2,3-dihydro-oenanthotoxin formed [M+H](+) and its methanol adduct. The two polyalkynes could be chromatographically resolved on C18 by gradient elution with aqueous acetonitrile. This provides superior analysis for oenanthotoxin using HPLC with photodiode array (PDA) detection alone, but for LC-MS/MS aqueous acetonitrile was less suitable due to poor ionisation and, with APCI, an increase in the relative abundance of a [M-1](+) species, which could confuse compound assignment. HPLC-PDA and LC-MS/MS methods using an aqueous acetonitrile or aqueous methanol mobile phase, respectively, were successful when applied to the analysis of the stomach contents of a pony suspected to have eaten O. crocata. Relevant product ion spectra, by ion trap MS/MS, accurate mass data and complete sets of (1)H and (13)C NMR spectral assignments are given for the two compounds.     

Protein structure similarity from principle component correlation analysis

Background  

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.     

Conditional random pattern model for copy number aberration detection

Background  

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.     

Evidence against Stable Protein S-Nitrosylation as a Widespread Mechanism of Post-translational Regulation

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Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Background

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.

Results

Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.

Conclusions

We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.