John Frederick Graf

Case-Based Reasoning in Color Matching

A case-based reasoning system for determining what colorants to use for producing a specific color of plastic was created. The selection of colorants needs to take many factors into consideration. A technique that involved fuzzy logic was used to compare the quality of the color match for each factor. The system has been in use for two years at a growing number of GE Plastics sites and has shown significant cost savings.

The effect of hydrogen bonding on the phase behaviour of ternary polymer blends

In this paper theoretical and experimental studies of the phase behaviour of ternary polymer blends are reviewed. Particular emphasis is placed upon the effect of specific interactions (hydrogen bonds). The association model developed to predict the phase behaviour of binary hydrogen-bonded polymer blends has been extended to ternary polymer blends. Simulations have been performed to illustrate the major effects of ‘physical’ (primarily dispersion) and ‘chemical’ (hydrogen bonding) forces on phase behaviour. Theoretically predicted phase diagrams have also been compared to experimental results obtained from five hydrogen-bonded ternary polymer blend systems. Overall, the agreement is satisfactory. One general conclusion is that it will be very difficult to find ternary polymer blends that exist in a single phase over a wide composition range. Furthermore, in most cases an immiscible binary blend cannot be made homogeneous by introducing a small amount of a third polymer (‘compatibilizer’).

Blood protein predictors of brain amyloid for enrichment in clinical trials

Measures of neocortical amyloid burden (NAB) identify individuals who are at substantially greater risk of developing Alzheimers disease (AD). Blood‐based biomarkers predicting NAB would have great utility for the enrichment of AD clinical trials, including large‐scale prevention trials.

Specific Interactions and the Miscibility of Polymer Blends: Practical Guides For Predicting & Designing Miscible Polymer Mixtures

Method of producing quenched and tempered hollow steel structural members of polygonal cross section includes the steps of hot rolling a hollow steel tube, austenitizing the tube, water quenching it, reheating the tube to a tempering temperature just below the lower critical transformation temperature, and rolling the tube to the desired polygonal cross section while holding the temperature of the member at the tempering temperature.

Blood-Based Biomarker Candidates of Cerebral Amyloid Using PiB PET in Non-Demented Elderly

Increasingly, clinical trials for Alzheimers disease (AD) are being conducted earlier in the disease phase and with biomarker confirmation using in vivo amyloid PET imaging or CSF tau and Aβ measures to quantify pathology. However, making such a pre-clinical AD diagnosis is relatively costly and the screening failure rate is likely to be high. Having a blood-based marker that would reduce such costs and accelerate clinical trials through identifying potential participants with likely pre-clinical AD would be a substantial advance. In order to seek such a candidate biomarker, discovery phase proteomic analyses using 2DGE and gel-free LC-MS/MS for high and low molecular weight analytes were conducted on longitudinal plasma samples collected over a 12-year period from non-demented older individuals who exhibited a range of 11C-PiB PET measures of amyloid load. We then sought to extend our discovery findings by investigating whether our candidate biomarkers were also associated with brain amyloid burden in disease, in an independent cohort. Seven plasma proteins, including A2M, Apo-A1, and multiple complement proteins, were identified as pre-clinical biomarkers of amyloid burden and were consistent across three time points (p <  0.05). Five of these proteins also correlated with brain amyloid measures at different stages of the disease (q <  0.1). Here we show that it is possible to detect a plasma based biomarker signature indicative of AD pathology at a stage long before the onset of clinical disease manifestation. As in previous studies, acute phase reactants and inflammatory markers dominate this signature.

BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems

We developed a detailed, whole-body physiologically based pharmacokinetic (PBPK) modeling tool for calculating the distribution of pharmaceutical agents in the various tissues and organs of a human or animal as a function of time. Ordinary differential equations (ODEs) represent the circulation of body fluids through organs and tissues at the macroscopic level, and the biological transport mechanisms and biotransformations within cells and their organelles at the molecular scale. Each major organ in the body is modeled as composed of one or more tissues. Tissues are made up of cells and fluid spaces. The model accounts for the circulation of arterial and venous blood as well as lymph. Since its development was fueled by the need to accurately predict the pharmacokinetic properties of imaging agents, BioDMET is more complex than most PBPK models. The anatomical details of the model are important for the imaging simulation endpoints. Model complexity has also been crucial for quickly adapting the tool to different problems without the need to generate a new model for every problem. When simpler models are preferred, the non-critical compartments can be dynamically collapsed to reduce unnecessary complexity. BioDMET has been used for imaging feasibility calculations in oncology, neurology, cardiology, and diabetes. For this purpose, the time concentration data generated by the model is inputted into a physics-based image simulator to establish imageability criteria. These are then used to define agent and physiology property ranges required for successful imaging. BioDMET has lately been adapted to aid the development of antimicrobial therapeutics. Given a range of built-in features and its inherent flexibility to customization, the model can be used to study a variety of pharmacokinetic and pharmacodynamic problems such as the effects of inter-individual differences and disease-states on drug pharmacokinetics and pharmacodynamics, dosing optimization, and inter-species scaling. While developing a tool to aid imaging agent and drug development, we aimed at accelerating the acceptance and broad use of PBPK modeling by providing a free mechanistic PBPK software that is user friendly, easy to adapt to a wide range of problems even by non-programmers, provided with ready-to-use parameterized models and benchmarking data collected from the peer-reviewed literature.

Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.

Feasibility of imaging myelin lesions in multiple sclerosis

The goal of this study was to provide a feasibility assessment for PET imaging of multiple sclerosis (MS) lesions based on their decreased myelin content relative to the surrounding normal-appearing brain tissue. The imaging agent evaluated for this purpose is a molecule that binds strongly and specifically to myelin basic protein. Physiology-based pharmacokinetic modeling combined with PET image simulation applied to a brain model was used to examine whether such an agent would allow the differentiation of artificial lesions 4–10 mm in diameter from the surrounding normal-looking white and gray matter. Furthermore, we examined how changes in agent properties, model parameters, and experimental conditions can influence imageability, identifying a set of conditions under which imaging of MS lesions might be feasible. Based on our results, we concluded that PET imaging has the potential to become a useful complementary method to MRI for MS diagnosis and therapy monitoring.

Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures

Background Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view. Results The Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence. Conclusions MOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information).

Immunization Elicits Antigen-Specific Antibody Sequestration in Dorsal Root Ganglia Sensory Neurons

The immune and nervous systems are two major organ systems responsible for host defense and memory. Both systems achieve memory and learning that can be retained, retrieved, and utilized for decades. Here, we report the surprising discovery that peripheral sensory neurons of the dorsal root ganglia (DRGs) of immunized mice contain antigen-specific antibodies. Using a combination of rigorous molecular genetic analyses, transgenic mice, and adoptive transfer experiments, we demonstrate that DRGs do not synthesize these antigen-specific antibodies, but rather sequester primarily IgG1 subtype antibodies. As revealed by RNA-seq and targeted quantitative PCR (qPCR), dorsal root ganglion (DRG) sensory neurons harvested from either naïve or immunized mice lack enzymes (i.e., RAG1, RAG2, AID, or UNG) required for generating antibody diversity and, therefore, cannot make antibodies. Additionally, transgenic mice that express a reporter fluorescent protein under the control of Igγ1 constant region fail to express Ighg1 transcripts in DRG sensory neurons. Furthermore, neural sequestration of antibodies occurs in mice rendered deficient in neuronal Rag2, but antibody sequestration is not observed in DRG sensory neurons isolated from mice that lack mature B cells [e.g., Rag1 knock out (KO) or μMT mice]. Finally, adoptive transfer of Rag1-deficient bone marrow (BM) into wild-type (WT) mice or WT BM into Rag1 KO mice revealed that antibody sequestration was observed in DRG sensory neurons of chimeric mice with WT BM but not with Rag1-deficient BM. Together, these results indicate that DRG sensory neurons sequester and retain antigen-specific antibodies released by antibody-secreting plasma cells. Coupling this work with previous studies implicating DRG sensory neurons in regulating antigen trafficking during immunization raises the interesting possibility that the nervous system collaborates with the immune system to regulate antigen-mediated responses.