Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH- and CCSNa+) of emerging contaminants using Multiple Adaptive Regression Splines

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or non-target approaches (i.e. when no reference standards are available) there is no information on retention time (RT) and collision cross section (CCS) values to facilitate identification. In-silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) was evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the empirical data. The RT model (R2=0.855) showed a deviation between predicted and empirical data of ± 2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using CCSH model (R2=0.966) was ± 4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules resulting in deviations below ± 5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2=0.954) with deviation below ± 5.25% for the 95% of the cases. The developed models have been incorporated in an open access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library

Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact––increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.

Effect of Hydraulic Retention Time on the Performance of a Compact Moving Bed Biofilm Reactor for Effluent Polishing of Treated Sewage

Treated effluent from a wastewater treatment plant can be further reused as a water resource for a water supply treatment plant. In this case, the treated sewage gathered in the study of the Class V National Water Quality Standard (NWQS) of Malaysia would be treated for use as a water resource for a water treatment plant. In a moving bed biofilm reactor (MBBR) with a 500-L working volume, organic pollutants, undesirable nutrients, and bacteria were removed without disinfectant. At 24-h hydraulic retention time (HRT), the maximum removal efficiency of 5-day biological oxygen demand, ammonia–nitrogen (NH3-N), and total phosphorus were 71%, 48%, and 12%, respectively. The biofilm thickness, which was captured using scanning electron microscopy, increased from 102.6 μm (24-h HRT) to 297.1 μm (2-h HRT). A metagenomic analysis using 16S rRNA showed an abundance of anaerobic bacteria, especially from the Proteobacteria phylum, which made up almost 53% of the total microbes. MBBR operated at 24-h HRT could improve effluent quality, as its characteristics fell into Class IIA of the NWQS of Malaysia, with the exception of the NH3-N content, which indicated that the effluent needed conventional treatment prior to being reused as potable water.

Versatile Memristor for Memory and Neuromorphic Computing

The memristor is a promising candidate to implement high-density memory and neuromorphic computing. Based on the retention time, memristors are classified into volatile and non-volatile types. However, a single memristor...