Paradoxical response of plasma atrial natriuretic hormone to pericardiocentesis in cardiac tamponade

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26804/1/0000360.pd

Cooperation between Mast Cells and Neurons Is Essential for Antigen-Mediated Bronchoconstriction

Mast cells are important sentinels guarding the interface between the environment and the body: a breach in the integrity of this interface can lead to the release of a plethora of mediators which engage the foreign agent, recruit leukocytes, and initiate adaptive physiological changes in the organism. While these capabilities make mast cells critical players in immune defense, it also makes them important contributors to the pathogenesis of diseases such as asthma. Mast cell mediators induce dramatic changes in smooth muscle physiology, and the expression of receptors for these factors by smooth muscle suggests that they act directly to initiate constriction. Contrary to this view, we show here that mast cell-mediated bronchoconstriction is observed only in animals with intact innervation of the lung and that serotonin release alone is required for this action. While ablation of sensory neurons does not limit bronchoconstriction, constriction after antigen challenge is absent in mice in which the cholinergic pathways are compromised. Linking mast cell function to the cholinergic system likely provides an important means of modulating the function of these resident immune cells to physiology of the lung, but may also provide a safeguard against life-threatening anaphylaxis during mast cell degranulation

Family-based association analysis of alcohol dependence criteria and severity

Background Despite the high heritability of alcohol dependence (AD), the genes found to be associated with it account for only a small proportion of its total variability. The goal of this study was to identify and analyze phenotypes based on homogeneous classes of individuals to increase the power to detect genetic risk factors contributing to the risk of AD. Methods The 7 individual DSM-IV criteria for AD were analyzed using latent class analysis (LCA) to identify classes defined by the pattern of endorsement of the criteria. A genome-wide association study was performed in 118 extended European American families (n = 2,322 individuals) densely affected with AD to identify genes associated with AD, with each of the seven DSM-IV criteria, and with the probability of belonging to two of three latent classes. Results Heritability for DSM-IV AD was 61%, and ranged from 17-60% for the other phenotypes. A SNP in the olfactory receptor OR51L1 was significantly associated (7.3 × 10−8) with the DSM-IV criterion of persistent desire to, or inability to, cut down on drinking. LCA revealed a three-class model: the “low risk” class (50%) rarely endorsed any criteria, and none met criteria for AD; the “moderate risk” class (33) endorsed primarily 4 DSM-IV criteria, and 48% met criteria for AD; the “high risk” class (17%) manifested high endorsement probabilities for most criteria and nearly all (99%) met criteria for AD One single nucleotide polymorphism (SNP) in a sodium leak channel NALCN demonstrated genome-wide significance with the high risk class (p=4.1 × 10−8). Analyses in an independent sample did not replicate these associations. Conclusion We explored the genetic contribution to several phenotypes derived from the DSM-IV alcohol dependence criteria. The strongest evidence of association was with SNPs in NALCN and OR51L1

Modeling recursive RNA interference.

An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off-switch on experiments

Plasma levels of immunoreactive atrial natriuretic factor increase during supraventricular tachycardia

A significant diuretic and natriuretic response occurs during paroxysmal supraventricular tachycardia (SVT). Although the diuresis may be secondary to suppression of vasopressin secretion, the etiology of the natriuresis remains unexplained. To determine if atrial natriuretic factor (ANF) could contribute to the polyuric response during SVT, 10 patients were studied: five during spontaneous SVT and five during simulated SVT produced by rapid simultaneous atrial and ventricular pacing. Plasma immunoreactive ANF (IR-ANF) levels measured by radioimmunoassay were obtained at baseline (before and/or 24 to 48 hours after SVT) and after at least 15 minutes of SVT in all patients. During spontaneous and simulated SVT, IR-ANF was significantly elevated (mean +/- SE; 275 +/- 68 pmol/L) compared to baseline (28 +/- 7 pmol/L; P = 0.0036). Similar increases in IR-ANF were noted during both simulated and spontaneous SVT. To determine if this IR-ANF release was related to the increase in heart rate or the rise in right atrial pressure during SVT, IR-ANF levels were also measured in five patients with sinus tachycardia and in six patients with congestive heart failure, IR-ANF was significantly related to right atrial pressure (r = 0.93; P = 0.0009) but not to heart rate (r = 0.46). Thus, IR-ANF is elevated during SVT and may contribute to the natriuretic response. The stimulus to IR-ANF secretion during SVT appears to be related to the rise in right atrial pressure rather than to the increase in heart rate.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26001/1/0000067.pd

Decision Process in Human-Agent Interaction: Extending Jason Reasoning Cycle

The main characteristic of an agent is acting on behalf of humans. Then, agents are employed as modeling paradigms for complex systems and their implementation. Today we are witnessing a growing increase in systems complexity, mainly when the presence of human beings and their interactions with the system introduces a dynamic variable not easily manageable during design phases. Design and implementation of this type of systems highlight the problem of making the system able to decide in autonomy. In this work we propose an implementation, based on Jason, of a cognitive architecture whose modules allow structuring the decision-making process by the internal states of the agents, thus combining aspects of self-modeling and theory of the min

Assessment of first and second degree relatives of individuals with bipolar disorder shows increased genetic risk scores in both affected relatives and young At‐Risk Individuals

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113761/1/ajmgb32344.pd

iSAM2 : incremental smoothing and mapping using the Bayes tree

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Sage for personal use, not for redistribution. The definitive version was published in International Journal of Robotics Research 31 (2012): 216-235, doi:10.1177/0278364911430419.We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.M. Kaess, H. Johannsson and J. Leonard were partially supported by ONR grants N00014-06-1-0043 and N00014-10-1-0936. F. Dellaert and R. Roberts were partially supported by NSF, award number 0713162, “RI: Inference in Large-Scale Graphical Models”. V. Ila has been partially supported by the Spanish MICINN under the Programa Nacional de Movilidad de Recursos Humanos de Investigación

Association of substance dependence phenotypes in the COGA sample

Alcohol and drug use disorders are individually heritable (50%). Twin studies indicate that alcohol and substance use disorders share common genetic influences, and therefore may represent a more heritable form of addiction and thus be more powerful for genetic studies. This study utilized data from 2322 subjects from 118 European-American families in the Collaborative Study on the Genetics of Alcoholism sample to conduct genome-wide association analysis of a binary and a continuous index of general substance dependence liability. The binary phenotype (ANYDEP) was based on meeting lifetime criteria for any DSM-IV dependence on alcohol, cannabis, cocaine or opioids. The quantitative trait (QUANTDEP) was constructed from factor analysis based on endorsement across the seven DSM-IV criteria for each of the four substances. Heritability was estimated to be 54% for ANYDEP and 86% for QUANTDEP. One single-nucleotide polymorphism (SNP), rs2952621 in the uncharacterized gene LOC151121 on chromosome 2, was associated with ANYDEP (P = 1.8 × 10(-8) ), with support from surrounding imputed SNPs and replication in an independent sample [Study of Addiction: Genetics and Environment (SAGE); P = 0.02]. One SNP, rs2567261 in ARHGAP28 (Rho GTPase-activating protein 28), was associated with QUANTDEP (P = 3.8 × 10(-8) ), and supported by imputed SNPs in the region, but did not replicate in an independent sample (SAGE; P = 0.29). The results of this study provide evidence that there are common variants that contribute to the risk for a general liability to substance dependence

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey