Type 1 diabetes (T1D) results from autoimmune destruction of β cells. Insufficient availability of biomarkers represents a significant gap in understanding the disease cause and progression. We conduct blinded, two-phase case-control plasma proteomics on the TEDDY study to identify biomarkers predictive of T1D development. Untargeted proteomics of 2,252 samples from 184 individuals identify 376 regulated proteins, showing alteration of complement, inflammatory signaling, and metabolic proteins even prior to autoimmunity onset. Extracellular matrix and antigen presentation proteins are differentially regulated in individuals who progress to T1D vs. those that remain in autoimmunity. Targeted proteomics measurements of 167 proteins in 6,426 samples from 990 individuals validate 83 biomarkers. A machine learning analysis predicts if individuals would remain in autoimmunity or develop T1D 6 months before autoantibody appearance, with areas under receiver operating characteristic curves of 0.871 and 0.918, respectively. Our study identifies and validates biomarkers, highlighting pathways affected during T1D development.

Interactions between circulating tumour cells (CTCs) and platelets are thought to inhibit natural killer(NK)-cell-induced lysis. We attempted to correlate CTC numbers in men with advanced prostate cancer with platelet counts and circulating lymphocyte numbers. Sixty-one ExPeCT trial participants, divided into overweight/obese and normal weight groups on the basis of a BMI ≥ 25 or <25, were randomized to participate or not in a six-month exercise programme. Blood samples at randomization, and at three and six months, were subjected to ScreenCell filtration, circulating platelet counts were obtained, and flow cytometry was performed on a subset of samples (n = 29). CTC count positively correlated with absolute total lymphocyte count (r2 = 0.1709, p = 0.0258) and NK-cell count (r2 = 0.49, p < 0.0001). There was also a positive correlation between platelet count and CTC count (r2 = 0.094, p = 0.0001). Correlation was also demonstrated within the overweight/obese group (n = 123, p < 0.0001), the non-exercise group (n = 79, p = 0.001) and blood draw samples lacking platelet cloaking (n = 128, p < 0.0001). By flow cytometry, blood samples from the exercise group (n = 15) had a higher proportion of CD3+ T-lymphocytes (p = 0.0003) and lower proportions of B-lymphocytes (p = 0.0264) and NK-cells (p = 0.015) than the non-exercise group (n = 14). These findings suggest that CTCs engage in complex interactions with the coagulation cascade and innate immune system during intravascular transit, and they present an attractive target for directed therapy at a vulnerable stage in metastasis.

The microbial and molecular characterization of the ectorhizosphere is an important step towards developing a more complete understanding of how the cultivation of biofuel crops can be undertaken in nutrient poor environments. The ectorhizosphere of Setaria is of particular interest because the plant component of this plant-microbe system is an important agricultural grain crop and a model for biofuel grasses. Importantly, Setaria lends itself to high throughput molecular studies. As such, we have identified important intra- and interspecific microbial and molecular differences in the ectorhizospheres of three geographically distant Setaria italica accessions and their wild ancestor S. viridis. All were grown in a nutrient-poor soil with and without nutrient addition. To assess the contrasting impact of nutrient deficiency observed for two S. italica accessions, we quantitatively evaluated differences in soil organic matter, microbial community, and metabolite profiles. Together, these measurements suggest that rhizosphere priming differs with Setaria accession, which comes from alterations in microbial community abundances, specifically Actinobacteria and Proteobacteria populations. When globally comparing the metabolomic response of Setaria to nutrient addition, plants produced distinctly different metabolic profiles in the leaves and roots. With nutrient addition, increases of nitrogen containing metabolites were significantly higher in plant leaves and roots along with significant increases in tyrosine derived alkaloids, serotonin, and synephrine. Glycerol was also found to be significantly increased in the leaves as well as the ectorhizosphere. These differences provide insight into how C4 grasses adapt to changing nutrient availability in soils or with contrasting fertilization schemas. Gained knowledge could then be utilized in plant enhancement and bioengineering efforts to produce plants with superior traits when grown in nutrient poor soils.

Primary biological aerosol particles (PBAPs) are microscopic solids suspended in the atmosphere emitted by biological systems and play critical roles in the atmosphere and the atmosphere-biosphere system, impacting human health, climate, and the ecosystem function. Understanding the sources of PBAPs is necessary to decipher the mechanistic interactions between aerosols, climate, and other ecosystem components. However, the detection of specific PBAPs in complex ambient aerosol samples is challenging. We performed metabolomics analyses of pollen from three pollinating tree species and ambient samples collected during the peak pollination period of each species. Random Forest and sPLS-DA machine learning methods were employed to evaluate whether metabolic signatures of ambient samples can reveal the source of the main pollen particles present in the atmosphere. Our results suggest that atmospheric ecometabolomics techniques combined with sophisticated statistical methods can decipher the origin of abundant PBAPs from complex ambient samples. Developing complete libraries containing high-resolution metabolomic fingerprints of the major PBAPs present in the atmosphere would significantly advance future research to accurately understand the role of PBAPs in the atmosphere, ecosystems, and human health.

Background: The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. TEDDY has collected heterogenous data longitudinally to gain insights into the environmental and biological mechanisms driving the progression to persistent islet autoantibodies. Methods: We developed a machine learning model to predict imminent transition to the development of persistent islet autoantibodies based on time-varying metabolomics data integrated with time-invariant risk factors (eg, gestational age). The machine learning was initiated with 221 potential features (85 genetic, 5 environmental, 131 metabolomic) and an ensemble-based feature evaluation was utilized to identify a small set of predictive features that can be interrogated to better understand the pathogenesis leading up to persistent islet autoimmunity. Results: The final integrative machine learning model included 42 disparate features, returning a cross-validated receiver operating characteristic area under the curve (AUC) of 0.74 and an AUC of similar to 0.65 on an independent validation dataset. The model identified a principal set of 20 time-invariant markers, including 18 genetic markers (16 single nucleotide polymorphisms [SNPs] and two HLA-DR genotypes) and two demographic markers (gestational age and exposure to a prebiotic formula). Integration with the metabolome identified 22 supplemental metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies. Conclusions: The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to three pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate, suggesting that these metabolic processes may play a role in triggering islet autoimmunity.

This work was performed for the U.S. Department of Energy (DOE) Office of River Protection (ORP) to provide expert evaluation and experimental work in support of the River Protection Project vitrification technology development1. The long-term objective of this work is to expand the property-composition database for Hanford site low-activity waste (LAW) glasses and property-composition models to cover the balance of the mission for the Hanford Waste Treatment and Immobilization Plant (WTP). When this effort is complete, enhanced LAW glass property-composition models will be developed.

Background Circulating tumour cells (CTCs) represent a morphologically distinct subset of cancer cells, which aid the metastatic spread. The ExPeCT trial aimed to examine the effectiveness of a structured exercise programme in modulating levels of CTCs and platelet cloaking in patients with metastatic prostate cancer. Methods Participants (n = 61) were randomised into either standard care (control) or exercise arms. Whole blood was collected for all participants at baseline (T0), three months (T3) and six months (T6), and analysed for the presence of CTCs, CTC clusters and platelet cloaking. CTC data was correlated with clinico-pathological information. Results Changes in CTC number were observed within group over time, however no significant difference in CTC number was observed between groups over time. Platelet cloaking was identified in 29.5% of participants. A positive correlation between CTC number and white cell count (WCC) was observed (p = 0.0001), in addition to a positive relationship between CTC clusters and PSA levels (p = 0.0393). Conclusion The presence of platelet cloaking has been observed in this patient population for the first time, in addition to a significant correlation between CTC number and WCC. Trial registration ClincalTrials.gov identifier NCT02453139 .

This article motivates, presents, and illustrates an approach using nonlinear logistic regression (NLR) for modeling binary response data from a mixture experiment when the components can be partitioned into groups used to form dimensionally reduced components (DRCs). A DRC is formed from a linear combination of the components in a group having similar roles and/or effects of the same sign, where the linear combinations over all groups are normalized so that the DRC proportions sum to one. Linear combinations of a particular form provide for quantifying the effects of the remaining components in a group relative to a chosen component. This reason, plus dimensional reduction, are the primary motivations for the proposed DRC mixture experiment modeling approach. NLR is required because models expressed in terms of the DRCs are nonlinear in the parameters that specify the linear combinations. A method for obtaining nonparametric tolerance limits on the probability of a “success” for the binary response variable using a bootstrap approach is also presented. Finally, the article shows how DRCs provide for visualizing data and modeling results that otherwise would be impossible. A real database on the presence or absence of nepheline crystals in simulated nuclear waste glass is used to illustrate the NLR modeling and nonparametric tolerance limit approaches. The methodology is general and can be applied to other applications.

243 Background: Physical activity has been described as an effective adjunct for improving quality of life in patients with metastatic prostate cancer (PrCa). Circulating tumor cells (CTCs) are a morphologically distinct set of cancer cells, involved in disease progression. Platelet cloaking, the surrounding and subsequent attachment of platelets to CTCs, has been proposed as effective in aiding extravasation, thus enhancing metastatic spread. The ExPeCT (Exercise, Prostate Cancer and Circulating Tumor Cells) trial aimed to examine the impact of a structured exercise intervention on the mechanisms of metastasis, including CTC cloaking, in patients with metastatic PrCa. Methods: ExPeCT was a multi-center, randomized trial for patients with metastatic PrCa (n=61). Participants were randomized to either control or exercise arms. Participants in the exercise arm (n=29) completed six months of prescribed aerobic exercise. Whole blood was collected for all participants at baseline (T0), three months (T3) and six months (T6), and analyzed for the presence of CTCs, CTC clusters and platelet cloaking. Results: No significant difference in CTC number between the exercise and control arms was observed (p=0.2630), however significant alterations in CTC number were observed within group over time (p<0.001). Platelet cloaking was modelled as a binary response (presence/absence). A trend towards higher levels of platelet cloaking in the control group was observed (p=0.1005). Further, all findings were compared to a range of clinical variables. CTC clusters were positively correlated with PSA levels (p=0.0393) and a positive correlation between CTC number and white blood cell count (p<0.001) was determined. Conclusions: No significant relationship between CTCs and physical activity was observed, however the exercise program was well tolerated by ExPeCT participants. This study has, for the first time, shown platelet cloaking of CTCs in metastatic PrCa, in addition to a significant positive correlation between CTCs and white blood cells. These findings provide critical insight into immune crosstalk and the metastatic cascade in metastatic PrCa. Clinical trial information: NCT02453139.

Subsampled image acquisition followed by image inpainting in a scanning transmission electron microscope is a novel approach to control dose and increase the image frame rate during experiments, thereby allowing independent control of the spatial and temporal dose envelope during image acquisition. Here, subsampled imaging is shown to permit precise in situ observations of the fundamental kinetic processes behind nucleation and growth of silver (Ag) nanoparticles from an aqueous solution. At high sampling-levels, nanoparticles can be observed with morphologies that are consistent with strong interface interactions, i.e., rafts and pillars, whereas at low sampling-levels, the particles exhibit regular spherical morphologies. The relative numbers of rafts/pillars and regular nanoparticles, their sizes, and their incubation times can be attributed to local changes in the molar concentration of the Ag ions in the aqueous solution; higher sampling-levels significantly increase the reactants in the vicinity of the window, leading to rapid supersaturation and the precipitation on the window surface. These precisely controlled kinetics highlight subsampled imaging as a method by which the driving force for nucleation and growth (i.e., the electron beam) can be disentangled from the spatial/temporal resolution of the observation in all in situ experiments, providing a pathway to identify and quantify the importance of individual kinetic factors behind nucleation and growth in a wide variety of complex materials systems and architectures.

We develop a methodology that combines stable principal component pursuit (SPCP) and elastic net regression to perform multivariate outlier and trend detection simultaneously. The SPCP framework detects both univariate and multivariate outliers by decomposing a data matrix into its sparse and low-rank components. Elastic net regression applied to the low-rank matrix identifies the response variables that are well-explained by a set of covariates without being affected by influential outliers. By combining these techniques into a single objective function, we simultaneously detect univariate and multivariate outliers and trend with an accompanying estimate of magnitude for each. Our methodology is applied to both real and synthetic data to show its value and accuracy.

Hanford low-activity waste (LAW) glasses with high sulfur concentrations are subject to salt segregation in the melter, which hinders melter operation by corroding components and shortening the melter life. To better predict the point at which salt accumulates on the melt surface, the development of sulfate solubility models is needed. Using a sulfur saturation method, crucible scale melts for 13 LAW glasses with varying sulfur solubilities were conducted. The resulting salt and glass compositions were reported and the change in component partitioning following the saturation process was examined to better understand potential changes in overall glass composition. It was shown that both Cr2O3 and Cl experience significant losses, with ~28% of Cr2O3 partitioning into the salt phase and Cl primarily volatilizing out of the melt (~23% partitioned to salt and ~40% lost as off gas). These patterns can be accounted for during model development. Measured sulfur solubilities were also compared to previously reported data. It was found that crucible sulfur solubility ranged from 0.95 to 2.14 wt% SO3 with a high correlation ([Rfit2=0.94]) between crucible solubility and melter tolerance. These results suggest that crucible scale sulfate solubility data can be used to predict SO3 tolerance in the melter feed.

Making efficient and timely inferences about data generated by real-time systems is challenging, as they often consist of high-volume, high-velocity data streams. In particular, when a user interacts with a real-time system to gain insights, detect events, and make decisions about the system, the rate and amount of information the user is required to process is generally overwhelming. In addition, analytically processing large volumes of data can be computationally expensive and, in real-time, renders traditional inferential methods effectively useless. One approach to mitigate these challenges is to reduce both the amount of information presented to the user and the volume of data placed in the stream. Similar to other multivariate quality control techniques, we will describe a method constructed specifically for high throughput images in addition to the development and deployment of an online tool, Real time Event Detector for Subsampled Images (REDSI), designed to provide feedback on a real-time system by characterizing and detecting events of interest. We will discuss REDSI in the context of scanning transmission electron microscopy (STEM), which is a powerful real-time system that provides high spatial and temporal resolution on nanoscale structures and processes. The data produced by in situ STEM experiments are a stream of images relaying structural, compositional, and dynamic interphase information to scientists in fields ranging from microbiology and neuroscience to materials science and energetics.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography-MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

Malignant pleural mesothelioma (MPM) is an aggressive inflammatory cancer with a poor survival rate. Treatment options are limited at best and drug resistance is common. Thus, there is an urgent need to identify novel therapeutic targets in this disease in order to improve patient outcomes and survival times. MST1R (RON) is a trans-membrane receptor tyrosine kinase (RTK), which is part of the c-MET proto-oncogene family. The only ligand recognized to bind MST1R (RON) is Macrophage Stimulating 1 (MST1), also known as Macrophage Stimulating Protein (MSP) or Hepatocyte Growth Factor-Like Protein (HGFL). In this study, we demonstrate that the MST1-MST1R (RON) signaling axis is active in MPM. Targeting this pathway with a small molecule inhibitor, LCRF-0004, resulted in decreased proliferation with a concomitant increase in apoptosis. Cell cycle progression was also affected. Recombinant MST1 treatment was unable to overcome the effect of LCRF-0004 in terms of either proliferation or apoptosis. Subsequently, the effect of an additional small molecular inhibitor, BMS-777607 (which targets MST1R (RON), MET, Tyro3, and Axl) also resulted in a decreased proliferative capacity of MPM cells. In a cohort of MPM patient samples, high positivity for total MST1R by IHC was an independent predictor of favorable prognosis. Additionally, elevated expression levels of MST1 also correlated with better survival. This study also determined the efficacy of LCRF-0004 and BMS-777607 in xenograft MPM models. Both LCRF-0004 and BMS-777607 demonstrated significant anti-tumor efficacy in vitro, however BMS-777607 was far superior to LCRF-0004. The in vivo and in vitro data generated by this study indicates that a multi-TKI, targeting the MST1R/MET/TAM signaling pathways, may provide a more effective therapeutic strategy for the treatment of MPM as opposed to targeting MST1R alone.