Exome sequencing identifies nonsegregating nonsense ATM and PALB2 variants in familial pancreatic cancer.

We sequenced 11 germline exomes from five families with familial pancreatic cancer (FPC). One proband had a germline nonsense variant in ATM with somatic loss of the variant allele. Another proband had a nonsense variant in PALB2 with somatic loss of the variant allele. Both variants were absent in a relative with FPC. These findings question the causal mechanisms of ATM and PALB2 in these families and highlight challenges in identifying the causes of familial cancer syndromes using exome sequencing

OWL2Vec*: Embedding of OWL Ontologies

Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies. In this paper, we propose a language model based ontology embedding method named OWL2Vec*, which encodes the semantics of an ontology by taking into account its graph structure, lexical information and logic constructors. Our empirical evaluation with three real world datasets suggests that OWL2Vec* benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, OWL2Vec* often significantly outperforms the state-of-the-art methods in our experiments

Application of Commercial Non-Dispersive Infrared Spectroscopy Sensors for Sub-Ambient Carbon Dioxide Detection

Monitoring carbon dioxide (CO2) concentration within a spacecraft or spacesuit is critically important to ensuring the safety of the crew. Carbon dioxide uniquely absorbs light at wavelengths of 3.95 micrometers and 4.26 micrometers. As a result, non-dispersive infrared (NDIR) spectroscopy can be employed as a reliable and inexpensive method for the quantification of CO2 within the atmosphere. A multitude of commercial-off-the-shelf (COTS) NDIR sensors exist for CO2 quantification. The COTS sensors provide reasonable accuracy so long as the measurements are attained under conditions close to the calibration conditions of the sensor (typically 21.1 C and 1 atm). However, as pressure deviates from atmospheric to the pressures associated with a spacecraft (8.0-10.2 PSIA) or spacesuit (4.1-8.0 PSIA), the error in the measurement grows increasingly large. In addition to pressure and temperature dependencies, the infrared transmissivity through a volume of gas also depends on the composition of the gas. As the composition is not known a priori, accurate sub-ambient detection must rely on iterative sensor compensation techniques. This manuscript describes the development of recursive compensation algorithms for sub-ambient detection of CO2 with COTS NDIR sensors. In addition, the basis of the exponential loss in accuracy is developed theoretically considering thermal, Doppler, and Lorentz broadening effects which arise as a result of the temperature, pressure, and composition of the gas mixture under analysis. As a result, this manuscript provides an approach to employing COTS sensors at sub-ambient conditions and may also lend insight into designing future NDIR sensors for aerospace application

Accumbens Cholinergic Interneurons Mediate Cue-Induced Nicotine Seeking and Associated Glutamatergic Plasticity

Nicotine, the primary addictive substance in tobacco, is widely abused. Relapse to cues associated with nicotine results in increased glutamate release within nucleus accumbens core (NAcore), modifying synaptic plasticity of medium spiny neurons (MSNs), which contributes to reinstatement of nicotine seeking. However, the role of cholinergic interneurons (ChIs) within the NAcore in mediating these neurobehavioral processes is unknown. ChIs represent less than 1% of the accumbens neuronal population and are activated during drug seeking and reward-predicting events. Thus, we hypothesized that ChIs may play a significant role in mediating glutamatergic plasticity that underlies nicotine-seeking behavior. Using chemogenetics in transgenic rats expressing Cre under the control of the choline acetyltransferase (ChAT) promoter, ChIs were bidirectionally manipulated before cue-induced reinstatement. Following nicotine self-administration and extinction, ChIs were activated or inhibited before a cue reinstatement session. Following reinstatement, whole-cell electrophysiology from NAcore MSNs was used to assess changes in plasticity, measured via AMPA/NMDA (A/N) ratios. Chemogenetic inhibition of ChIs inhibited cued nicotine seeking and resulted in decreased A/N, relative to control animals, whereas activation of ChIs was unaltered, demonstrating that ChI inhibition may modulate plasticity underlying cue-induced nicotine seeking. These results demonstrate that ChI neurons play an important role in mediating cue-induced nicotine reinstatement and underlying synaptic plasticity within the NAcore

Grouping time series by pairwise measures of redundancy

A novel approach is proposed to group redundant time series in the frame of causality. It assumes that (i) the dynamics of the system can be described using just a small number of characteristic modes, and that (ii) a pairwise measure of redundancy is sufficient to elicit the presence of correlated degrees of freedom. We show the application of the proposed approach on fMRI data from a resting human brain and gene expression profiles from HeLa cell culture.Comment: 4 pages, 8 figure

Large-scale inference and graph theoretical analysis of gene-regulatory networks in B. stubtilis

We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B. subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks' graph theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [17], we are able to identify trends in graph theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise.Comment: 22 pages, 4 figures, accepted for publication in Physica A (2006

Critical Ingredients of Consumer Run Services: Results of a National Survey

Fidelity criteria are increasingly used in program monitoring and evaluation, but are difficult to derive for emerging models (i.e., those not based on theory or a research demonstration project). We describe steps used to develop and operationalize fidelity criteria for consumer-run (CR) mental health services: articulating and operationalizing criteria based on published literature, then revising and validating the criteria through expert judgments using a modified Delphi method. Respondents rated highest those structural and process components emphasizing the value of consumerism: consumer control, consumer choices and opportunities for decision-making, voluntary participation (and the absence of coercion), and respect for members by staff.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44316/1/10597_2004_Article_480623.pd

Weak pairwise correlations imply strongly correlated network states in a neural population

Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah (http://cosyne.org