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A wide variety of electrophilic derivatives of itaconate, the Kreb’s cycle-derived metabolite, are immunomodulatory, yet these derivatives have overlapping and sometimes contradictory activities. Therefore, we generated a genetic system to interrogate the immunomodulatory functions of endogenously produced itaconate in human macrophages. Endogenous itaconate is driven by multiple innate signals restraining inflammatory cytokine production. Endogenous itaconate directly targets cysteine 13 in IRAK4 (disrupting IRAK4 autophosphorylation and activation), drives the degradation of nuclear factor κB, and modulates global ubiquitination patterns. As a result, cells unable to make itaconate overproduce inflammatory cytokines such as tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), and IL-1β in response to these innate activators. In contrast, the production of interferon (IFN)β, downstream of LPS, requires the production of itaconate. These data demonstrate that itaconate is a critical arbiter of inflammatory cytokine production downstream of multiple innate signaling pathways, laying the groundwork for the development of itaconate mimetics for the treatment of autoimmunity.

Aims and methodsAccurate protein measurements using formalin-fixed biopsies are needed to improve disease characterisation. This feasibility study used targeted and global mass spectrometry (MS) to interrogate a spectrum of disease severities using 19 ulcerative colitis (UC) biopsies.ResultsTargeted assays for CD8, CD19, CD132 (interleukin-2 receptor subunit gamma/common cytokine receptor gamma chain), FOXP3 (forkhead box P3) and IL17RA (interleukin 17 receptor A) were successful; however, assays for IL17A (interleukin 17A), IL23 (p19) (interleukin 23, alpha subunit p19) and IL23R (interleukin 23 receptor) did not permit target detection. Global proteome analysis (4200 total proteins) was performed to identify pathways associated with UC progression. Positive correlation was observed between histological scores indicating active colitis and neutrophil-related measurements (R2=0.42–0.72); inverse relationships were detected with cell junction targets (R2=0.49–0.71) and β-catenin (R2=0.51–0.55) attributed to crypt disruption. An exploratory accuracy assessment with Geboes Score and Robarts Histopathology Index cut-offs produced sensitivities/specificities of 72.7%/75.0% and 100.0%/81.8%, respectively.ConclusionsPathologist-guided MS assessments provide a complementary approach to histological scoring systems. Additional studies are indicated to verify the utility of this novel approach.

Mass spectrometry imaging is a powerful analytical technique for detecting and determining spatial distributions of molecules within a sample. Typically, mass spectrometry imaging is limited to the analysis of thin tissue sections taken from the middle of a sample. In this work, we present a mass spectrometry imaging method for the detection of compounds produced by bacteria on the outside surface of ant exoskeletons in response to pathogen exposure. Fungus-growing ants have a specialized mutualism with Pseudonocardia, a bacterium that lives on the ants’ exoskeletons and helps protect their fungal garden food source from harmful pathogens. The developed method allows for visualization of bacterial-derived compounds on the ant exoskeleton. This method demonstrates the capability to detect compounds that are specifically localized to the bacterial patch on ant exoskeletons, shows good reproducibility across individual ants, and achieves accurate mass measurements within 5 ppm error when using a high-resolution, accurate-mass mass spectrometer.

The Utah Department of Transportation (UDOT) implemented dynamic management of portable variable speed limit (PVSL) technology to reduce regulatory speed limits through an active work space (AWS). UDOT also developed and tested an intelligent system approach to alter speed limits in construction work zones. The goal of the PVSL system was to provide a portable and dynamic system that was easy for construction personnel to use to prudently reduce speeds within an AWS and make construction work zones safer for workers and the traveling public, while limiting the need to reduce speed throughout the AWS, rather than the entire construction work zone. This was achieved through temporary regulatory reductions in driver speeds within the immediate boundary of an AWS when workers were on site and exposed to the danger of errant vehicles during active construction. The system also raises speed limits when workers were not present. This PVSL system used a dynamic variable speed limit (VSL) algorithm to raise and lower the regulatory speed limits. The PVSL system also provided a queue warning algorithm that operated independent of the VSL algorithm to control messages posted on the portable variable message sign (PVMS) trailers to disseminate dynamic information to drivers. UDOT has completed 2 years of PVSL system deployment testing in four separate construction work zones to evaluate the effectiveness of the system. This paper highlights key elements that guided development of the PVSL system, along with the successful results from deployment of the system.

Plant science is an important, rapidly developing area of study. Within plant science, one area of study that has grown tremendously with recent technological advances, such as mass spectrometry, is the field of plant-omics; however, plant peptidomics is relatively underdeveloped in comparison with proteomics and metabolomics. Endogenous plant peptides can act as signaling molecules and have been shown to affect cell division, development, nodulation, reproduction, symbiotic associations, and defense reactions. There is a growing need to uncover the role of endogenous peptides on a molecular level. Mass spectrometric imaging (MSI) is a valuable tool for biological analyses as it allows for the detection of thousands of analytes in a single experiment and also displays spatial information for the detected analytes. Despite the prediction of a large number of plant peptides, their detection and imaging with spatial localization and chemical specificity is currently lacking. Here we analyzed the endogenous peptides and proteins in Medicago truncatulausing matrix-assisted laser desorption/ionization (MALDI)–MSI. Hundreds of endogenous peptides and protein fragments were imaged, with interesting peptide spatial distribution changes observed between plants in different developmental stages.

(RS)-3-(2-Carboxypiperazin-4-yl)-propyl-1-phosphonic acid (CPP) is a competitive antagonist of the N-methyl-d-aspartate (NMDA) receptor and is routinely used with rodent models to investigate the role of NMDA receptors in brain function. This highly polar compound is difficult to separate from biological matrices. A reliable and sensitive assay was developed for the determination of CPP in plasma and tissue. In order to overcome the challenges relating to the physicochemical properties of CPP we employed an initial separation using solid phase extraction harnessing mixed-mode anion exchange. Then an ion-pair UPLC C18 separation was performed followed by MS/MS with a Waters Acquity UPLC interfaced to an AB Sciex QTrap 5500 mass spectrometer, which was operated in positive ion ESI mode. Multiple reaction monitoring (MRM) mode was utilized to detect the analyte and internal standard. The precursor to product ions used for quantitation of CPP and internal standard were m/z252.958 → 207.100 and 334.955 → 136.033, respectively. This method was applied to a pharmacokinetic study and examined brain tissue and plasma concentrations following intravenous and intraperitoneal injections of CPP. The elimination half-life (t1/2) of CPP was 8.8 minutes in plasma and 14.3 minutes in brain tissue, and the plasma to brain concentration ratio was about 15 : 1. This pharmacokinetic data will aid the interpretation of the vast number of studies using CPP to investigate NMDA receptor function in rodents and the method itself can be used to study many other highly polar analytes of interest.

We analyse sequence and structural features of protein-RNA interfaces using RB-147, a non-redundant dataset of protein-RNA complexes extracted from the PDB. We train classifiers using machine learning algorithms to predict protein-RNA interfaces from sequence and structure-derived features of proteins. Our experiments show that Struct-NB, a Naive Bayes classifier that exploits structural features, outperforms its counterparts that use only sequence features to predict protein-RNA binding residues.

The prevalence of type 1 diabetes continues to increase worldwide at a rate higher than previously projected, while the number of patients achieving American Diabetes Association (ADA) glycated hemoglobin (A1c) goals remains suboptimal. There are numerous barriers to patients achieving A1c targets including increased frequency of severe hypoglycemia associated with lowering plasma glucose as measured by lower A1c values. Continuous glucose monitoring (CGM) was first approved for retrospective analysis and now has advanced to the next step in diabetes management with the approval of real-time glucose sensing. Real-time CGM, in short term studies, has been shown to decrease A1c values, improve glucose variability (GV), and minimize the time and number of hypoglycemic events in patients with type 1 diabetes. These products are approved for adjunctive use to self-monitoring of blood glucose (SMBG), but future long-term studies are needed to document their safety, efficacy, ability to replace SMBG as a tool of monitoring, and ultimately utility into closed-loop insulin delivery systems. New algorithms will need to be developed that account for rapid changes in the glucose values, so that accuracy of the sensor data can be maintained. In addition, for better clinical care and usage, algorithms also need to be developed for both patients and the providers to guide them for their ongoing diabetes care.

An understanding of the species that form in mixtures of alcohol and water is important for their use in liquid chromatography applications. In reverse-phase liquid chromatography the retention of solutes on a chromatography column is influenced by the composition of the mobile phase, and in the case of alcohol and water mobile phases, the amount of free alcohol and water present. Previous and similar modeling studies of methanol (MeOH) and water mixtures by near-infrared (NIR) spectroscopy have found up to four species present including free MeOH and water and MeOH and water complexes formed by hydrogen bonding associations. In this work an equilibrium model has been applied to NIR measurements of MeOH and water mixtures. A high-performance liquid chromatography (HPLC) pump was coupled to an NIR flow cell to produce a gradual change in mixture composition. This resulted in a greater mixture resolution than has been achieved previously by manual mixture preparation. It was determined that five species contributed to the data. An equilibria model consisting of MeOH, H2O, MeOH(H2O) (log KMeOHH2O= 0.10 ± 0.03), MeOH(H2O)4(log KMeOH4H2O= −2.14 ± 0.08), and MeOH(H2O)9(log KMeOH9H2O= −8.6 ± 0.1) was successfully fitted to the data. The model supports the results of previous work and highlights the progressive formation of MeOH and water complexes that occur with changing mixture composition. The model also supports that mixtures of MeOH and water are not simple binary mixtures and that this is responsible for observed deviations from expected elution behavior.

Two parametric bootstrap techniques were applied to near‐infrared (NIR) pattern classification models for two classes of microcrystalline cellulose, Avicel® PH101 and PH102, which differ only in particle size. The development of pattern classification models for similar substances is difficult, since their characteristic clusters overlap. Bootstrapping was used to enlarge small test sets for a better approximation of the overlapping area of these nearly identical substances, consequently resulting in better estimates of misclassification rates. A bootstrap that resampled the residuals, referred to as the outside model space bootstrap in this paper, and a novel bootstrap that resampled principal component scores, referred to as the inside model space bootstrap, were studied. A comparison revealed that classification rates for both bootstrap techniques were similar to the original test set classification rates. The bootstrap method developed in this study, which resampled the principal component scores, was more effective for estimating misclassification volumes than the residual‐resampling method. Copyright © 2002 John Wiley & Sons, Ltd.

Multivariate optical elements (MOEs) are multilayer optical interference coatings with arbitrary spectral profiles that are used in multivariate pattern recognition to perform the task of projecting magnitudes of special basis functions (regression vectors) out of optical spectra. Because MOEs depend on optical interference effects, their performance is sensitive to the angle of incidence of incident light. This angle dependence complicates their use in imaging applications. We report a method for the design of angle-insensitive MOEs based on modification of a previously described nonlinear optimization algorithm. This algorithm operates when the effects of deviant angles of incidence are simulated prior to optimization, which treats the angular deviation as an interferent in the measurement. To demonstrate the algorithm, a 13-layer imaging MOE (IMOE, with alternating layers of high-index Nb_2O_5 and low-index SiO_2) for the determination of Bismarck Brown dye in mixtures of Bismarck Brown and Crystal Violet, was designed and its performance simulated. For angles of incidence that range from 42° to 48°, the IMOE has an average standard error of prediction (SEP) of 0.55 µM for Bismarck Brown. This compares with a SEP of 2.8 µM for a MOE designed by a fixed-angle algorithm.

A new algorithm for the design of optical computing filters for chemical analysis, otherwise known as multivariate optical elements (MOEs), is described. The approach is based on the nonlinear optimization of the MOE layer thicknesses to minimize the standard error in sample prediction for the chemical species of interest using a modified version of the Gauss–Newton nonlinear optimization algorithm. The design algorithm can either be initialized with random layer thicknesses or with layer thicknesses derived from spectral matching of a multivariate principal component regression (PCR) vector for the constituent of interest. The algorithm has been successfully tested by using it to design various MOEs for the determination of Bismarck Brown dye in a binary mixture of Crystal Violet and Bismarck Brown.

This paper reports a new multivariate method of background correction for hyphenated chromatography data such as diode array HPLC and diode array capillary electrophoresis (CE) measured under conditions of low signal-to-noise. The new method is able to correct linear and curved dynamically shifting baselines, a significant problem that limits the precision of CE assays. Its use is illustrated with nine simulated and 63 measured data sets. Serial dilutions were used to give measured data sets with a signal-to-noise ratio down to 10:1. The method's usefulness for routine operation was demonstrated by applying it to the above 72 data sets in an ‘automatic’ mode without any user interaction. In many cases these experiments showed improved precision and quantitative accuracy of peak areas after multivariate background correction. In most other cases the background correction method was neutral, i.e. it did not harm the peak area accuracy and precision where background offsets were absent or were too small to be reliably corrected. Compared with second-order calibration methods such as the generalized rank annihilation method (GRAM) which can also correct for unknown background signals, the new method is insensitive to variation in peak shape and retention time from run to run, and prior knowledge in the form of standards is not needed. Copyright © 1999 John Wiley & Sons, Ltd.

A wavelength calibration method for a charge-coupled device (CCD) array detector and seven-channel fiber-optic spectrograph is described. The method was developed for the extraction of wavelength-calibrated spectra for in situdissolution testing of tablets. The method includes automatic recognition of the positions of the seven fiber channels and recognition of mercury line positions in pixel numbers. A wavelength calibration model with two trigonometric terms was used for least-squares fitting of horizontal pixel numbers to the known wavelengths of five lines from a low-pressure mercury discharge lamp. Four other models were used for comparison. The standard error of estimate (SEE) was minimum for the model with two trigonometric terms. Each fiber channel was calibrated separately. After least-squares fitting, linear interpolation was used to obtain the wavelength-calibrated spectra in the range of 190–450 nm at 1-nm intervals. With this method, spectra from seven fiber-optic probes can be acquired simultaneously and rapidly for in situdissolution testing. The wavelength calibration procedure is good enough to permit solution spectra acquired on one channel to be used for multivariate calibration of the other channels under appropriate circumstances.

A principal components regression (PCR) is used to construct a calibration matrix for multicomponent spectroscopic analysis which corrects for background absorption due to variable concentrations of unknown species in a sample matrix. Mixed standards are used in the method, and spectra of the pure background components are not needed. The background correction capability of the method is demonstrated with the use of the UV spectra of aqueous standards prepared from cobalt (II) nitrate and nickel (II) nitrate. Known concentrations of chromium (III) nitrate were added to simulate a background which gave rise to relative concentration errors as high as 70% without the background correction capabilities of the method. With the background correction capabilities of the method, the relative concentration errors were reduced to less than one percent. Preliminary results are presented from an application of PCR to determine the concentration of two active ingredients in a pharmaceutical product in the presence of weakly absorbing tablet excipient materials.