Time Varying Dimension Models

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US inflation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.

Hierarchical Shrinkage in Time-Varying Parameter Models

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.

Computing Markov-Perfect Optimal Policies in Business-Cycle Models

Time-inconsistency is an essential feature of many policy problems (Kydland and Prescott, 1977). This paper presents and compares three methods for computing Markov-perfect optimal policies in stochastic nonlinear business cycle models. The methods considered include value function iteration, generalized Euler-equations, and parameterized shadow prices. In the context of a business cycle model in which a scal authority chooses government spending and income taxation optimally, while lacking the ability to commit, we show that the solutions obtained using value function iteration and generalized Euler equations are somewhat more accurate than that obtained using parameterized shadow prices. Among these three methods, we show that value function iteration can be applied easily, even to environments that include a risk-sensitive scal authority and/or inequality constraints on government spending. We show that the risk-sensitive scal authority lowers government spending and income-taxation, reducing the disincentive households face to accumulate wealth.

Proteomic analysis of cytokine induced proteins in human intestinal epithelial cells: implications for inflammatory bowel diseases.

A role for cytokine regulated proteins in epithelial cells has been suggested in the pathogenesis of inflammatory bowel diseases (IBD). The aim of this study was to identify such cytokine regulated targets using a proteomic functional approach. Protein patterns from (35)S-radiolabeled homogenates of cultured colon epithelial cells were compared before and after exposure to interferon-gamma, interleukin-1beta and interleukin-6. Proteins were separated by two-dimensional polyacrylamide gel electrophoresis. Both autoradiographies and silver stained gels were analyzed. Proteins showing differential expression were identified by tryptic in-gel digestion and mass spectrometry. Metabolism related proteins were also investigated by Western blot analysis. Tryptophanyl-tRNA synthetase, indoleamine-2,3-dioxygenase, heterogeneous nuclear ribonucleoprotein JKTBP, interferon-induced 35kDa protein, proteasome subunit LMP2 and arginosuccinate synthetase were identified as cytokine modulated proteins in vitro. Using purified epithelial cells from patients, overexpression of indoleamine-2,3-dioxygenase, an enzyme involved in tryptophan metabolism, was confirmed in Crohn's disease as well as in ulcerative colitis, as compared to normal mucosa. No such difference was found in diverticulitis. Potentially, this observation opens new avenues in the treatment of IBD.

Superfamily of steroid nuclear receptors: positive and negative regulators of gene expression.

The nuclear hormone receptor superfamily is characterized by an impressive functional diversity of its members despite a remarkable overall structural unity. A variety of ligands bind specifically to them and these receptors control gene networks that have profound effects on growth, development, and homeostasis. The ligand-receptor complexes recognize transcriptional enhancer DNA sequences, the hormone response elements, resulting in induction or repression of gene activity. The similarity between all these hormone response enhancer elements, as well as between the receptors themselves, indicates a conserved general strategy for the hormonal control of transcription by steroids. The activated receptors bind to responsive promoters and most likely mediate the assembly of stage- and tissue-specific transcription factor complexes that stimulate or inhibit gene expression.

Development of nuclear DNA probes for the typing of Trypanososma cruzi

We have developed and tested a new way of typing Trypanosoma cruzi, mamely the use of cloned nuclear DNA fragments as genetic markers. Restriction fragment length polymorphisms were verified on Soutern blots hybridized to random probes. Fragment patterns were analyzed and dendrograms constructed. Our results on well characterized laboratory strains correlate well to published isoenzyme studies. Some of the probes were also hybridized to chromosomes separated by pulse field gel electrophoresis a higher degree of heterogeneity was observed at this level.

In vivo conditional Pax4 overexpression in mature islet β-cells prevents stress-induced hyperglycemia in mice.

OBJECTIVE To establish the role of the transcription factor Pax4 in pancreatic islet expansion and survival in response to physiological stress and its impact on glucose metabolism, we generated transgenic mice conditionally and selectively overexpressing Pax4 or a diabetes-linked mutant variant (Pax4R129 W) in β-cells. RESEARCH DESIGN AND METHODS Glucose homeostasis and β-cell death and proliferation were assessed in Pax4- or Pax4R129 W-overexpressing transgenic animals challenged with or without streptozotocin. Isolated transgenic islets were also exposed to cytokines, and apoptosis was evaluated by DNA fragmentation or cytochrome C release. The expression profiles of proliferation and apoptotic genes and β-cell markers were studied by immunohistochemistry and quantitative RT-PCR. RESULTS Pax4 but not Pax4R129 W protected animals against streptozotocin-induced hyperglycemia and isolated islets from cytokine-mediated β-cell apoptosis. Cytochrome C release was abrogated in Pax4 islets treated with cytokines. Interleukin-1β transcript levels were suppressed in Pax4 islets, whereas they were increased along with NOS2 in Pax4R129 W islets. Bcl-2, Cdk4, and c-myc expression levels were increased in Pax4 islets while MafA, insulin, and GLUT2 transcript levels were suppressed in both animal models. Long-term Pax4 expression promoted proliferation of a Pdx1-positive cell subpopulation while impeding insulin secretion. Suppression of Pax4 rescued this defect with a concomitant increase in pancreatic insulin content. CONCLUSIONS Pax4 protects adult islets from stress-induced apoptosis by suppressing selective nuclear factor-κB target genes while increasing Bcl-2 levels. Furthermore, it promotes dedifferentiation and proliferation of β-cells through MafA repression, with a concomitant increase in Cdk4 and c-myc expression.

Structured and unstructured continuous models for Wolbachia infections

We introduce and investigate a series of models for an infection of a diplodiploid host species by the bacterial endosymbiont Wolbachia. The continuous models are characterized by partial vertical transmission, cytoplasmic incompatibility and fitness costs associated with the infection. A particular aspect of interest is competitions between mutually incompatible strains. We further introduce an age-structured model that takes into account different fertility and mortality rates at different stages of the life cycle of the individuals. With only a few parameters, the ordinary differential equation models exhibit already interesting dynamics and can be used to predict criteria under which a strain of bacteria is able to invade a population. Interestingly, but not surprisingly, the age-structured model shows significant differences concerning the existence and stability of equilibrium solutions compared to the unstructured model.

Low gene copy number shows that arbuscular mycorrhizal fungi inherit genetically different nuclei.

Arbuscular mycorrhizal fungi (AMF) are ancient asexually reproducing organisms that form symbioses with the majority of plant species, improving plant nutrition and promoting plant diversity. Little is known about the evolution or organization of the genomes of any eukaryotic symbiont or ancient asexual organism. Direct evidence shows that one AMF species is heterokaryotic; that is, containing populations of genetically different nuclei. It has been suggested, however, that the genetic variation passed from generation to generation in AMF is simply due to multiple chromosome sets (that is, high ploidy). Here we show that previously documented genetic variation in Pol-like sequences, which are passed from generation to generation, cannot be due to either high ploidy or repeated gene duplications. Our results provide the clearest evidence so far for substantial genetic differences among nuclei in AMF. We also show that even AMF with a very large nuclear DNA content are haploid. An underlying principle of evolutionary theory is that an individual passes on one or half of its genome to each of its progeny. The coexistence of a population of many genomes in AMF and their transfer to subsequent generations, therefore, has far-reaching consequences for understanding genome evolution.

PSMA-Specific CAR-Engineered T Cells Eradicate Disseminated Prostate Cancer in Preclinical Models.

Immunology-based interventions have been proposed as a promising curative chance to effectively attack postoperative minimal residual disease and distant metastatic localizations of prostate tumors. We developed a chimeric antigen receptor (CAR) construct targeting the human prostate-specific membrane antigen (hPSMA), based on a novel and high affinity specific mAb. As a transfer method, we employed last-generation lentiviral vectors (LV) carrying a synthetic bidirectional promoter capable of robust and coordinated expression of the CAR molecule, and a bioluminescent reporter gene to allow the tracking of transgenic T cells after in vivo adoptive transfer. Overall, we demonstrated that CAR-expressing LV efficiently transduced short-term activated PBMC, which in turn were readily stimulated to produce cytokines and to exert a relevant cytotoxic activity by engagement with PSMA+ prostate tumor cells. Upon in vivo transfer in tumor-bearing mice, CAR-transduced T cells were capable to completely eradicate a disseminated neoplasia in the majority of treated animals, thus supporting the translation of such approach in the clinical setting.

Protective role of peroxisome proliferator-activated receptor-β/δ in septic shock.

Rationale: Peroxisome proliferator activated receptor (PPAR)-beta/delta is a transcription factor that belongs to the PPAR nuclear hormone receptor family, but the role of PPAR-beta/delta in sepsis is unknown. Objectives: We investigated the role of PPAR-beta/delta in murine models of LPS-induced organ injury and dysfunction and cecal ligation and puncture (CLP)-induced polymicrobial sepsis. Methods: Wild-type (WT) and PPAR-beta/delta knockout (1(0) mice and C57BL/6 mice were subjected to LPS for 16 hours. C57BL/6 mice received the PPAR-beta/delta agonist GW0742 (0.03 mg/kg intravenously, 1 h after LPS) or GW0742 plus the PPAR-beta/delta antagonist GSK0660 (0.1 mg/kg intravenously, 30 min before LPS). CD-1 mice subjected to CLP received GW0742 or GW0742 plus GSK0660. Measurements and Main Results: In PPAR-beta/delta KO mice, endotoxemia exacerbated organ injury and dysfunction (cardiac, renal, and hepatic) and inflammation (lung) compared with WT mice. In C57BL/6 mice subjected to endotoxemia, GW0742 significantly (1) attenuated organ (cardiac and renal) dysfunction and inflammation (lung); (2) increased the phosphorylation of Akt and glycogen synthase kinase (GSK)-3 beta; (3) attenuated the increase in extracellular signal-regulated kinase (ERK)1/2 and signal transducer and activator of transcription (STAT)-3 phosphorylation; and (4) attenuated the activation of nuclear factor (NF)-kappa B and the expression of inducible nitric oxide synthase (iNOS). In CD-1 mice subjected to CLP, GW0742 improved 10-day survival. All the observed beneficial effects of GW0742 were attenuated by the PPAR-beta/delta antagonist GSK0660. Conclusions: PPAR-beta/delta protects against multiple organ injury and dysfunction, and inflammation caused by endotoxic shock and improves survival in polymicrobial sepsis by a mechanism that may involve activation of Akt and inhibition of GSK-3 beta and NF-kappa B.

The role of fixed delays in neuronal rate models

Fixed delays in neuronal interactions arise through synaptic and dendritic processing. Previous work has shown that such delays, which play an important role in shaping the dynamics of networks of large numbers of spiking neurons with continuous synaptic kinetics, can be taken into account with a rate model through the addition of an explicit, fixed delay. Here we extend this work to account for arbitrary symmetric patterns of synaptic connectivity and generic nonlinear transfer functions. Specifically, we conduct a weakly nonlinear analysis of the dynamical states arising via primary instabilities of the stationary uniform state. In this way we determine analytically how the nature and stability of these states depend on the choice of transfer function and connectivity. While this dependence is, in general, nontrivial, we make use of the smallness of the ratio in the delay in neuronal interactions to the effective time constant of integration to arrive at two general observations of physiological relevance. These are: 1 - fast oscillations are always supercritical for realistic transfer functions. 2 - Traveling waves are preferred over standing waves given plausible patterns of local connectivity.

Design and analysis of ChIP-Seq experiments for nuclear factor I DNA-binding proteins

SUMMARY : Eukaryotic DNA interacts with the nuclear proteins using non-covalent ionic interactions. Proteins can recognize specific nucleotide sequences based on the sterical interactions with the DNA and these specific protein-DNA interactions are the basis for many nuclear processes, e.g. gene transcription, chromosomal replication, and recombination. New technology termed ChIP-Seq has been recently developed for the analysis of protein-DNA interactions on a whole genome scale and it is based on immunoprecipitation of chromatin and high-throughput DNA sequencing procedure. ChIP-Seq is a novel technique with a great potential to replace older techniques for mapping of protein-DNA interactions. In this thesis, we bring some new insights into the ChIP-Seq data analysis. First, we point out to some common and so far unknown artifacts of the method. Sequence tag distribution in the genome does not follow uniform distribution and we have found extreme hot-spots of tag accumulation over specific loci in the human and mouse genomes. These artifactual sequence tags accumulations will create false peaks in every ChIP-Seq dataset and we propose different filtering methods to reduce the number of false positives. Next, we propose random sampling as a powerful analytical tool in the ChIP-Seq data analysis that could be used to infer biological knowledge from the massive ChIP-Seq datasets. We created unbiased random sampling algorithm and we used this methodology to reveal some of the important biological properties of Nuclear Factor I DNA binding proteins. Finally, by analyzing the ChIP-Seq data in detail, we revealed that Nuclear Factor I transcription factors mainly act as activators of transcription, and that they are associated with specific chromatin modifications that are markers of open chromatin. We speculate that NFI factors only interact with the DNA wrapped around the nucleosome. We also found multiple loci that indicate possible chromatin barrier activity of NFI proteins, which could suggest the use of NFI binding sequences as chromatin insulators in biotechnology applications. RESUME : L'ADN des eucaryotes interagit avec les protéines nucléaires par des interactions noncovalentes ioniques. Les protéines peuvent reconnaître les séquences nucléotidiques spécifiques basées sur l'interaction stérique avec l'ADN, et des interactions spécifiques contrôlent de nombreux processus nucléaire, p.ex. transcription du gène, la réplication chromosomique, et la recombinaison. Une nouvelle technologie appelée ChIP-Seq a été récemment développée pour l'analyse des interactions protéine-ADN à l'échelle du génome entier et cette approche est basée sur l'immuno-précipitation de la chromatine et sur la procédure de séquençage de l'ADN à haut débit. La nouvelle approche ChIP-Seq a donc un fort potentiel pour remplacer les anciennes techniques de cartographie des interactions protéine-ADN. Dans cette thèse, nous apportons de nouvelles perspectives dans l'analyse des données ChIP-Seq. Tout d'abord, nous avons identifié des artefacts très communs associés à cette méthode qui étaient jusqu'à présent insoupçonnés. La distribution des séquences dans le génome ne suit pas une distribution uniforme et nous avons constaté des positions extrêmes d'accumulation de séquence à des régions spécifiques, des génomes humains et de la souris. Ces accumulations des séquences artéfactuelles créera de faux pics dans toutes les données ChIP-Seq, et nous proposons différentes méthodes de filtrage pour réduire le nombre de faux positifs. Ensuite, nous proposons un nouvel échantillonnage aléatoire comme un outil puissant d'analyse des données ChIP-Seq, ce qui pourraient augmenter l'acquisition de connaissances biologiques à partir des données ChIP-Seq. Nous avons créé un algorithme d'échantillonnage aléatoire et nous avons utilisé cette méthode pour révéler certaines des propriétés biologiques importantes de protéines liant à l'ADN nommés Facteur Nucléaire I (NFI). Enfin, en analysant en détail les données de ChIP-Seq pour la famille de facteurs de transcription nommés Facteur Nucléaire I, nous avons révélé que ces protéines agissent principalement comme des activateurs de transcription, et qu'elles sont associées à des modifications de la chromatine spécifiques qui sont des marqueurs de la chromatine ouverte. Nous pensons que lés facteurs NFI interagir uniquement avec l'ADN enroulé autour du nucléosome. Nous avons également constaté plusieurs régions génomiques qui indiquent une éventuelle activité de barrière chromatinienne des protéines NFI, ce qui pourrait suggérer l'utilisation de séquences de liaison NFI comme séquences isolatrices dans des applications de la biotechnologie.

Combining food web and species distribution models for improved community projections

The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradient is subject to the interplay of biotic interactions in complement to abiotic environmental filtering. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose to use food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant-herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve both species distribution and community forecasts. Most importantly, this combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may be recurrent. Our combined approach points a promising direction forward to model the spatial variation of entire species interaction networks. Our work has implications for studies of range shifting species and invasive species biology where it may be unknown how a given biota might interact with a potential invader or in future climate.