This User’s Manual describes the code module MULTI-PRED, written in FORTRAN which implements the methodology for “predictive modeling of coupled multi-physics systems (PM-CMPS)” formulated by Cacuci (2014). This methodology fully takes into account the coupling terms between the systems but requires only the computational resources that would be needed to perform predictive modeling on each system separately. The PM-CMPS methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution based on a priori known mean values and uncertainties characterizing the experimental and computational parameters and results of interest responses, called for the multi-physics models under consideration. This “maximum entropy” a priori distribution is combined, using Bayes’ theorem, with the “likelihood” provided by the multi-physics simulation models to obtain a formal posterior distribution. Subsequently, the posterior distribution thus obtained is evaluated using the saddle-point method to obtain analytical expressions for the optimally predicted values for the multi-physics models parameters and responses along with corresponding reduced uncertainties. Noteworthy, the predictive modeling methodology for the coupled systems is constructed such that the systems can be considered sequentially rather than simultaneously, while preserving exactly the same results as if the systems were treated simultaneously. Consequently, very large coupled systems, which could perhaps exceed available computational resources if treated simultaneously, can be treated with the PM-CMPS methodology presented in this work sequentially and without any loss of generality or information, requiring just the resources that would be needed if the systems were treated sequentially. Three illustrative demonstration problems are also provided. The first problem presents the application of the PM-CMPS methodology to a simple particle diffusion problem which admits a closed-form analytical solution which facilitates a rapid understanding of this methodology and its predicted results. The second demonstration problem presents the application of the PM-CMPS methodology to the problem of inverse prediction, from detector responses in the presence of counting uncertainties, of the thickness of a homogeneous slab of material containing uniformly distributed gamma-emitting sources, for optically thin and thick slabs. This problem highlights the essential role played by the relative uncertainties (or, conversely, accuracies) of measured and computed responses. The third demonstration problem presents the application of the PM-CMPS methodology to the F-area cooling towers at the Savannah River National Lab. This problem demonstrates that the PM-CMPS methodology reduces the predicted response uncertainties not only at locations where measurements are available, but also at locations where measurements are not available.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies. It is typically detected at an advanced stage, at which the therapeutic options are very limited. One remarkable feature of PDAC that contributes to its resilience to treatment is the extreme stromal activation seen in these tumors. Often, the vast majority of tumor bulk consists of non-tumor cells that together provide a tumor-promoting environment. One of the signals that maintains and activates the stroma is the developmental protein Sonic Hedgehog (SHH). As the disease progresses, tumor cells produce increasing amounts of SHH, which activates the surrounding stroma to aid in tumor progression. To better understand this response and identify targets for inhibition, we aimed to elucidate the proteins that mediate the SHH-driven stromal response in PDAC. For this a novel mixed-species coculture model was set up in which the cancer cells are human, and the stroma is modeled by mouse fibroblasts. In conjunction with next-generation sequencing we were able to use the sequence difference between these species to genetically distinguish between the epithelial and stromal responses to SHH. The stromal SHH-dependent genes from this analysis were validated and their relevance for human disease was subsequently determined in two independent patient cohorts. In non-microdissected tissue from PDAC patients, in which a large amount of stroma is present, the targets were confirmed to associate with tumor stroma versus normal pancreatic tissue. Patient survival analysis and immunohistochemistry identified CDA, EDIL3, ITGB4, PLAUR and SPOCK1 as SHH-dependent stromal factors that are associated with poor prognosis in PDAC patients. Summarizing, the presented data provide insight into the role of the activated stroma in PDAC, and how SHH acts to mediate this response. In addition, the study has yielded several candidates that are interesting therapeutic targets for a disease for which treatment options are still inadequate.

Rbfox1 is a splicing regulator that has been associated with various neurological conditions such as autism spectrum disorder, mental retardation, epilepsy, attention-deficit/hyperactivity disorder and schizophrenia. We show that in adult rodent retinas, Rbfox1 is expressed in all types of retinal ganglion cells (RGCs) and in certain subsets of amacrine cells (ACs), within the inner nuclear (INL) and ganglion cell (GCL) layers. In the INL, all Rbfox1-positive cells were colocalized with GABAergic ACs, however not all GABAergic ACs were immunostained for Rbfox1. In the GCL, a vast majority of GABAergic dACs were Rbfox1-immunopositive. Furthermore, all cholinergic starburst ACs (SACs) in the INL (type a) and in the GCL (type b) were Rbfox1 positive. The expression of Rbfox1 in the retina significantly overlapped with expression of Rbfox2, another member of Rbfox family of proteins. Rbfox2, in addition to RGCs and ACs, was also expressed in horizontal cells. In developing retinas at E12 and E15, Rbfox1 is localized to the cytoplasm of differentiating RGCs and ACs. Between P0 and P5, Rbfox1 subcellular localization switched from cytoplasmic to predominantly nuclear. Downregulation of Rbfox1 in adult Rbfox1loxP/loxP mice had no detectable effect on retinal gross morphology. However, the visual cliff test revealed marked abnormalities of depth perception of these animals. RNA sequencing of retinal transcriptomes of control and Rbfox1 knockout animals identified a number of Rbfox1-regulated genes that are involved in establishing neuronal circuits and synaptic transmission, including Vamp1, Vamp2, Snap25, Trak2, and Slc1A7, suggesting the role of Rbfox1 in facilitating synaptic communications between ACs and RGCs.

A 73-year-old man was displaying symptoms of massive gastrointestinal (GI) bleed. Surgical actions were performed to control the bleed caused by an erosive duodenal ulcer with duodenal perforation. When investigating the culprit of this case, the pain medications prescribed two weeks prior by a traditional Chinese medicine doctor raised attention. The patient's admission serum sample and the pain medications from unknown sources were analyzed using a clinically validated liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method. The NSAIDs diclofenac, piroxicam, and indomethacin were identified, as well as some other synthetic drugs and natural products. The patient's concurrent exposure to multiple NSAIDs significantly increased the risk of upper GI complications. It is reasonable to argue that the high-dose use of the NSAIDs was a major cause of the duodenal ulcer and GI bleed. In addition, the identified natural products such as atropine and ephedrine have well-documented toxicities. It is important to increase the visibility of unregulated medications, and the capability to perform untargeted mass spectrometry analysis provides a unique diagnostic advantage in cases where exposure to toxic substances is possible.

To understand visual functions mediated by intrinsically photosensitive melanopsin-expressing retinal ganglion cells (mRGCs), it is important to elucidate axonal projections from these cells into the brain. Initial studies reported that melanopsin is expressed only in retinal ganglion cells within the eye. However, recent studies in Opn4-Cre mice revealed Cre-mediated marker expression in multiple brain areas. These discoveries complicate the use of melanopsin-driven genetic labeling techniques to identify retinofugal projections specifically from mRGCs. To restrict labeling to mRGCs, we developed a recombinant adeno-associated virus (AAV) carrying a Cre-dependent reporter (human placental alkaline phosphatase) that was injected into the vitreous of Opn4-Cre mouse eyes. The labeling observed in the brain of these mice was necessarily restricted specifically to retinofugal projections from mRGCs in the injected eye. We found that mRGCs innervate multiple nuclei in the basal forebrain, hypothalamus, amygdala, thalamus and midbrain. Midline structures tended to be bilaterally innervated, whereas the lateral structures received mostly contralateral innervation. As validation of our approach, we found projection patterns largely corresponded with previously published results; however, we have also identified a few novel targets. Our discovery of projections to the central amygdala suggests a possible direct neural pathway for aversive responses to light in neonates. In addition, projections to the accessory optic system suggest that mRGCs play a direct role in visual tracking, responses that were previously attributed to other classes of retinal ganglion cells. Moreover, projections to the zona incerta raise the possibility that mRGCs could regulate visceral and sensory functions. However, additional studies are needed to investigate the actual photosensitivity of mRGCs that project to the different brain areas. Also, there is a concern of "overlabeling" with very sensitive reporters that uncover low levels of expression. Light-evoked signaling from these cells must be shown to be of sufficient sensitivity to elicit physiologically relevant responses.

We do not have a full understanding of the mechanisms underlying plasticity in the human brain. Mouse models have well controlled environments and genetics, and provide tools to help dissect the mechanisms underlying the observed responses to therapies devised for humans recovering from injury of ischemic nature or trauma. We aimed to detect plasticity following learning of a unilateral reaching movement, and relied on MRI performed with a rapid structural protocol suitable for in vivo brain imaging, and a longer diffusion tensor imaging (DTI) protocol executed ex vivo. In vivo MRI detected contralateral volume increases in trained animals (reachers), in circuits involved in motor control, sensory processing, and importantly, learning and memory. The temporal association area, parafascicular and mediodorsal thalamic nuclei were also enlarged. In vivo MRI allowed us to detect longitudinal effects over the ~25 days training period. The interaction between time and group (trained versus not trained) supported a role for the contralateral, but also the ipsilateral hemisphere. While ex vivo imaging was affected by shrinkage due to the fixation, it allowed for superior resolution and improved contrast to noise ratios, especially for subcortical structures. We examined microstructural changes based on DTI, and identified increased fractional anisotropy and decreased apparent diffusion coefficient, predominantly in the cerebellum and its connections. Cortical thickness differences did not survive multiple corrections, but uncorrected statistics supported the contralateral effects seen with voxel based volumetric analysis, showing thickening in the somatosensory, motor and visual cortices. In vivo and ex vivo analyses identified plasticity in circuits relevant to selecting actions in a sensory-motor context, through exploitation of learned association and decision making. By mapping a connectivity atlas into our ex vivo template we revealed that changes due to skilled motor learning occurred in a network of 35 regions, including the primary and secondary motor (M1, M2) and sensory cortices (S1, S2), the caudate putamen (CPu), visual (V1) and temporal association cortex. The significant clusters intersected tractography based networks seeded in M1, M2, S1, V1 and CPu at levels > 80%. We found that 89% of the significant cluster belonged to a network seeded in the contralateral M1, and 85% to one seeded in the contralateral M2. Moreover, 40% of the M1 and S1 cluster by network intersections were in the top 80th percentile of the tract densities for their respective networks. Our investigation may be relevant to studies of rehabilitation and recovery, and points to widespread network changes that accompany motor learning that may have potential applications to designing recovery strategies following brain injury.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies. It is typically detected at an advanced stage, at which the therapeutic options are very limited. One remarkable feature of PDAC that contributes to its resilience to treatment is the extreme stromal activation seen in these tumors. Often, the vast majority of tumor bulk consists of non-tumor cells that together provide a tumor-promoting environment. One of the signals that maintains and activates the stroma is the developmental protein Sonic Hedgehog (SHH). As the disease progresses, tumor cells produce increasing amounts of SHH, which activates the surrounding stroma to aid in tumor progression. To better understand this response and identify targets for inhibition, we aimed to elucidate the proteins that mediate the SHH-driven stromal response in PDAC. For this a novel mixed-species coculture model was set up in which the cancer cells are human, and the stroma is modeled by mouse fibroblasts. In conjunction with next-generation sequencing we were able to use the sequence difference between these species to genetically distinguish between the epithelial and stromal responses to SHH. The stromal SHH-dependent genes from this analysis were validated and their relevance for human disease was subsequently determined in two independent patient cohorts. In non-microdissected tissue from PDAC patients, in which a large amount of stroma is present, the targets were confirmed to associate with tumor stroma versus normal pancreatic tissue. Patient survival analysis and immunohistochemistry identified CDA, EDIL3, ITGB4, PLAUR and SPOCK1 as SHH-dependent stromal factors that are associated with poor prognosis in PDAC patients. Summarizing, the presented data provide insight into the role of the activated stroma in PDAC, and how SHH acts to mediate this response. In addition, the study has yielded several candidates that are interesting therapeutic targets for a disease for which treatment options are still inadequate.

To understand multifactorial conditions such as Alzheimer's disease (AD) we need brain signatures that predict the impact of multiple pathologies and their interactions. To help uncover the relationships between pathology affected brain circuits and cognitive markers we have used mouse models that represent, at least in part, the complex interactions altered in AD, while being raised in uniform environments and with known genotype alterations. In particular, we aimed to understand the relationship between vulnerable brain circuits and memory deficits measured in the Morris water maze, and we tested several predictive modeling approaches. We used in vivo manganese enhanced MRI traditional voxel based analyses to reveal regional differences in volume (morphometry), signal intensity (activity), and magnetic susceptibility (iron deposition, demyelination). These regions included hippocampus, olfactory areas, entorhinal cortex and cerebellum, as well as the frontal association area. The properties of these regions, extracted from each of the imaging markers, were used to predict spatial memory. We next used eigenanatomy, which reduces dimensionality to produce sets of regions that explain the variance in the data. For each imaging marker, eigenanatomy revealed networks underpinning a range of cognitive functions including memory, motor function, and associative learning, allowing the detection of associations between context, location, and responses. Finally, the integration of multivariate markers in a supervised sparse canonical correlation approach outperformed single predictor models and had significant correlates to spatial memory. Among a priori selected regions, expected to play a role in memory dysfunction, the fornix also provided good predictors, raising the possibility of investigating how disease propagation within brain networks leads to cognitive deterioration. Our cross-sectional results support that modeling approaches integrating multivariate imaging markers provide sensitive predictors of AD-like behaviors. Such strategies for mapping brain circuits responsible for behaviors may help in the future predict disease progression, or response to interventions.

Retinal ganglion cells (RGCs) relay visual information from the eye to the brain. RGCs are the first cell type generated during retinal neurogenesis. Loss of function of the transcription factor Atoh7, expressed in multipotent early neurogenic retinal progenitors leads to a selective and essentially complete loss of RGCs. Therefore, Atoh7 is considered essential for conferring competence on progenitors to generate RGCs. Despite the importance of Atoh7 in RGC specification, we find that inhibiting apoptosis in Atoh7-deficient mice by loss of function of Bax only modestly reduces RGC numbers. Single-cell RNA sequencing of Atoh7;Bax-deficient retinas shows that RGC differentiation is delayed but that the gene expression profile of RGC precursors is grossly normal. Atoh7;Bax-deficient RGCs eventually mature, fire action potentials, and incorporate into retinal circuitry but exhibit severe axonal guidance defects. This study reveals an essential role for Atoh7 in RGC survival and demonstrates Atoh7-dependent and Atoh7-independent mechanisms for RGC specification.