Towards the analysis of high molecular weight proteins and protein complexes using TIMS-MS

In the present work, we demonstrate the potential and versatility of TIMS for the analysis of proteins, DNA-protein complexes and protein-protein complexes in their native and denatured states. In addition, we show that accurate CCS measurement are possible using internal and external mobility calibration and in good agreement with previously reported CCS values using other IMS analyzers (<5 % difference). The main challenges for the TIMS-MS analysis of high mass proteins and protein complexes in the mobility and m/z domain are described. That is, the analysis of high molecular weight systems in their native state may require the use of higher electric fields or a small compromise in the TIMS mobility resolution by reducing the bath gas velocity in order to effectively trap at lower electric fields. This is the first report of CCS measurements of high molecular weight biomolecules and biomolecular complexes (~150 kDa) using TIMS-MS.     

Re-investigation of the La–Mg phase diagram

Literature results on the La–Mg binary system are critically reviewed. It is shown that most recent experimental results are not in agreement with the previously published assessments of the system. Thus, an experimental re-investigation of this phase diagram is required. Several alloys are synthesized from pure constitutive elements and characterized using X-ray diffraction, scanning electron microscopy, and differential thermal analysis. This study clarifies the phase equilibria in the La–Mg system. These results combined with the recent study of the enthalpies of formation of the intermediate compounds of this system provide a sound basis for the Calphad optimization of the system.     

A social-event based approach to sentiment analysis of identities and behaviors in text

We describe a new methodology to infer sentiments held toward identities and behaviors from social events that we extract from a large corpus of newspaper text. Our approach draws on affect control theory, a mathematical model of how sentiment is encoded in social events and culturally shared views toward identities and behaviors. While most sentiment analysis approaches evaluate concepts on a single, evaluative dimension, our work extracts a three-dimensional sentiment “profile” for each concept. We can also infer when multiple sentiment profiles for a concept are likely to exist. We provide a case study of a large newspaper corpus on the Arab Spring, which helps to validate our approach.     

Quantitative thin-layer chromatographic measurement of n-trifluoroacetyladriamycin-14-valerate (AD 32) and trifluoroacetyladriamycin (AD 41) in blood and tissues

A thin-layer chromatographic method has been developed for the detection and measurement of N-trifluoroacetyladriamycin-14-valerate (AD 32) and its major metabolite trifluoroacetyladriamycin (AD 41). The procedure gives satisfactory linearity over a large range of concentrations. The coefficient of variability is about 10% over the entire range of usable concentrations, giving good reproducibility; sensitivity is 25 ng for both AD 32 and AD 41. Analysis is specific for AD 32 and AD 41 since adriamycin or more polar metabolites can be differentiated. Recovery is high (85-90%) and the method is simple and economical to use. Pharmacokinetics of AD 32 and AD 41 are reported in blood and some tissues of mice bearing Lewis Lung carcinoma.     

Predictivity of QSAR

A range of good quality, local QSARs for mutagenicity and carcinogenicity have been assessed and challenged for their predictivity in respect to real external test sets (i.e., chemicals never considered by the authors while developing their models). The QSARs for potency (applicable only to toxic chemicals) generated predictions 30-70% correct, whereas the QSARs for discriminating between active and inactive chemicals were 70-100% correct in their external predictions: thus the latter can be used with good reliability for applicative purposes. On the other hand internal, statistical validation methods, which are often assumed to be good diagnostics for predictivity, did not correlate well with the predictivity of the QSARs when challenged in external prediction tests. Nonlocal models for noncongeneric chemicals were considered as well, pointing to the critical role of an adequate definition of the applicability domain.     

Interrupted heating DTA for liquidus temperature determination of Ag–Cd–In alloys

Journal of Thermal Analysis and Calorimetry - An Ag–Cd–In alloy is used as the control rod material in most pressurized water reactors. Despite this important application, the phase...     

Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150–250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMS-CID-TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10–200 nM range, while simultaneously achieving discovery measurements of not initially targeted peptides as markers from the same proteins and, eventually, other proteins.

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AI and SAR approaches for predicting chemical carcinogenicity: survey and status report

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and properties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.     

Prediction of the rodent carcinogenicity of 60 pesticides by the DEREKfW expert system

The two-year rodent bioassay represents the golden standard for evaluating the carcinogenicity of chemicals. Because of practical and ethical reasons, alternative approaches have been investigated for many years. Among these approaches, the (quantitative) structure-activity relationships [(Q)SARs] offer promising perspectives for quickly screening a large number of chemicals. To increase the acceptance of (Q)SARs among the regulators, their predictive power needs to be scientifically validated. In this article, we tested the capacity of the DEREKfW expert system to qualitatively predict the rodent carcinogenicity and the genotoxic potential of 60 pesticides recently registered in Switzerland. The percentage of false negatives was found to be 31% for carcinogenicity. The associated sensitivity of 69% indicates that most of the pesticides with positive rodent bioassay results were detected by DEREKfW. On the other hand, the low specificity of 47% indicates that many pesticides may be flagged as carcinogenic while rodent bioassays would not confirm this potential. This may lead to unnecessary testing or the unnecessary restriction of a chemical.