Functional correction in mouse models of muscular dystrophy using exon-skipping tricyclo-DNA oligomers

Antisense oligonucleotides (AONs) hold promise for therapeutic correction of many genetic diseases via exon skipping, and the first AON-based drugs have entered clinical trials for neuromuscular disorders1, 2. However, despite advances in AON chemistry and design, systemic use of AONs is limited because of poor tissue uptake, and recent clinical reports confirm that sufficient therapeutic efficacy has not yet been achieved. Here we present a new class of AONs made of tricyclo-DNA (tcDNA), which displays unique pharmacological properties and unprecedented uptake by many tissues after systemic administration. We demonstrate these properties in two mouse models of Duchenne muscular dystrophy (DMD), a neurogenetic disease typically caused by frame-shifting deletions or nonsense mutations in the gene encoding dystrophin3, 4 and characterized by progressive muscle weakness, cardiomyopathy, respiratory failure5 and neurocognitive impairment6. Although current naked AONs do not enter the heart or cross the blood-brain barrier to any substantial extent, we show that systemic delivery of tcDNA-AONs promotes a high degree of rescue of dystrophin expression in skeletal muscles, the heart and, to a lesser extent, the brain. Our results demonstrate for the first time a physiological improvement of cardio-respiratory functions and a correction of behavioral features in DMD model mice. This makes tcDNA-AON chemistry particularly attractive as a potential future therapy for patients with DMD and other neuromuscular disorders or with other diseases that are eligible for exon-skipping approaches requiring whole-body treatment.

Behavioral Implications of Knockout for the Dyslexia-Risk Gene Dcdc2 in Mice

Several genetic linkage and epidemiological studies have provided strong evidence that DCDC2 is a candidate gene for developmental dyslexia, a disorder that impairs a person’s reading ability despite adequate intelligence, education, and socio-economic status. Studies investigating embryonic intra-ventricular RNA interference (RNAi) of Dcdc2, a rat homolog of the DCDC2 gene in humans, indicate disruptions in neuronal migration in the rat cortex during development. Interestingly, these anatomical anomalies are consistent with post mortem histological analysis of human dyslexic patients. Other rodent models of cortical developmental disruption have shown impairment in rapid auditory processing and learning maze tasks in affected subjects. The current study investigates the rapid auditory processing abilities of mice heterozygous for Dcdc2 (one functioning Dcdc2 allele) and mice with a homozygous knockout of Dcdc2 (no functioning Dcdc2 allele). It is important to note that this genetic model for behavioral assessment is still in the pilot stage. However, preliminary results suggest that mice with a genetic mutation of Dcdc2 have impaired rapid auditory processing, as well as non-spatial maze learning and memory ability, as compared to wildtypes. By genetically knocking out Dcdc2 in mice, behavioral features associated with Dcdc2 can be characterized, along with other neurological abnormalities that may arise due to the loss of the functioning gene.

Extracting more out of relocation data: building movement models as mixtures of random walks

We present a framework for fitting multiple random walks to animal movement paths consisting of ordered sets of step lengths and turning angles. Each step and turn is assigned to one of a number of random walks, each characteristic of a different behavioral state. Behavioral state assignments may be inferred purely from movement data or may include the habitat type in which the animals are located. Switching between different behavioral states may be modeled explicitly using a state transition matrix estimated directly from data, or switching probabilities may take into account the proximity of animals to landscape features. Model fitting is undertaken within a Bayesian framework using the WinBUGS software. These methods allow for identification of different movement states using several properties of observed paths and lead naturally to the formulation of movement models. Analysis of relocation data from elk released in east-central Ontario, Canada, suggests a biphasic movement behavior: elk are either in an "encamped" state in which step lengths are small and turning angles are high, or in an "exploratory" state, in which daily step lengths are several kilometers and turning angles are small. Animals encamp in open habitat (agricultural fields and opened forest), but the exploratory state is not associated with any particular habitat type.

The Devil’s Calculus: Mathematical Models of Civil War

In spite of the movement to turn political science into a real science, various mathematical methods that are now the staples of physics, biology, and even economics are thoroughly uncommon in political science, especially the study of civil war. This study seeks to apply such methods - specifically, ordinary differential equations (ODEs) - to model civil war based on what one might dub the capabilities school of thought, which roughly states that civil wars end only when one side’s ability to make war falls far enough to make peace truly attractive. I construct several different ODE-based models and then test them all to see which best predicts the instantaneous capabilities of both sides of the Sri Lankan civil war in the period from 1990 to 1994 given parameters and initial conditions. The model that the tests declare most accurate gives very accurate predictions of state military capabilities and reasonable short term predictions of cumulative deaths. Analysis of the model reveals the scale of the importance of rebel finances to the sustainability of insurgency, most notably that the number of troops required to put down the Tamil Tigers is reduced by nearly a full order of magnitude when Tiger foreign funding is stopped. The study thus demonstrates that accurate foresight may come of relatively simple dynamical models, and implies the great potential of advanced and currently unconventional non-statistical mathematical methods in political science.

Bayesian model selection of structural explanatory models: Application to road accident data

Using the Bayesian approach as the model selection criteria, the main purpose in this study is to establish a practical road accident model that can provide a better interpretation and prediction performance. For this purpose we are using a structural explanatory model with autoregressive error term. The model estimation is carried out through Bayesian inference and the best model is selected based on the goodness of fit measures. To cross validate the model estimation further prediction analysis were done. As the road safety measures the number of fatal accidents in Spain, during 2000-2011 were employed. The results of the variable selection process show that the factors explaining fatal road accidents are mainly exposure, economic factors, and surveillance and legislative measures. The model selection shows that the impact of economic factors on fatal accidents during the period under study has been higher compared to surveillance and legislative measures.

Sight distance studies on roads: influence of digital elevation models and roadside elements

Sight distance plays an important role in road traffic safety. Two types of Digital Elevation Models (DEMs) are utilized for the estimation of available sight distance in roads: Digital Terrain Models (DTMs) and Digital Surface Models (DSMs). DTMs, which represent the bare ground surface, are commonly used to determine available sight distance at the design stage. Additionally, the use of DSMs provides further information about elements by the roadsides such as trees, buildings, walls or even traffic signals which may reduce available sight distance. This document analyses the influence of three classes of DEMs in available sight distance estimation. For this purpose, diverse roads within the Region of Madrid (Spain) have been studied using software based on geographic information systems. The study evidences the influence of using each DEM in the outcome as well as the pros and cons of using each model.

Intrastriatal injection of an adenoviral vector expressing glial-cell-line-derived neurotrophic factor prevents dopaminergic neuron degeneration and behavioral impairment in a rat model of Parkinson disease

Glial-cell-line-derived neurotrophic factor (GDNF) is a potent neurotrophic factor for adult nigral dopamine neurons in vivo. GDNF has both protective and restorative effects on the nigro-striatal dopaminergic (DA) system in animal models of Parkinson disease. Appropriate administration of this factor is essential for the success of its clinical application. Since it cannot cross the blood–brain barrier, a gene transfer method may be appropriate for delivery of the trophic factor to DA cells. We have constructed a recombinant adenovirus (Ad) encoding GDNF and injected it into rat striatum to make use of its ability to infect neurons and to be retrogradely transported by DA neurons. Ad-GDNF was found to drive production of large amounts of GDNF, as quantified by ELISA. The GDNF produced after gene transfer was biologically active: it increased the survival and differentiation of DA neurons in vitro. To test the efficacy of the Ad-mediated GDNF gene transfer in vivo, we used a progressive lesion model of Parkinson disease. Rats received injections unilaterally into their striatum first of Ad and then 6 days later of 6-hydroxydopamine. We found that mesencephalic nigral dopamine neurons of animals treated with the Ad-GDNF were protected, whereas those of animals treated with the Ad-β-galactosidase were not. This protection was associated with a difference in motor function: amphetamine-induced turning was much lower in animals that received the Ad-GDNF than in the animals that received Ad-β-galactosidase. This finding may have implications for the development of a treatment for Parkinson disease based on the use of neurotrophic factors.

The role of the D2 dopamine receptor (D2R) in A2A adenosine receptor (A2AR)-mediated behavioral and cellular responses as revealed by A2A and D2 receptor knockout mice

The A2AR is largely coexpressed with D2Rs and enkephalin mRNA in the striatum where it modulates dopaminergic activity. Activation of the A2AR antagonizes D2R-mediated behavioral and neurochemical effects in the basal ganglia through a mechanism that may involve direct A2AR–D2R interaction. However, whether the D2R is required for the A2AR to exert its neural function is an open question. In this study, we examined the role of D2Rs in A2AR-induced behavioral and cellular responses, by using genetic knockout (KO) models (mice deficient in A2ARs or D2Rs or both). Behavioral analysis shows that the A2AR agonist 2–4-(2-carboxyethyl)phenethylamino-5′-N-ethylcarboxamidoadenosine reduced spontaneous as well as amphetamine-induced locomotion in both D2 KO and wild-type mice. Conversely, the nonselective adenosine antagonist caffeine and the A2AR antagonist 8-(3-chlorostyryl)caffeine produced motor stimulation in mice lacking the D2R, although the stimulation was significantly attentuated. At the cellular level, A2AR inactivation counteracted the increase in enkephalin expression in striatopallidal neurons caused by D2R deficiency. Consistent with the D2 KO phenotype, A2AR inactivation partially reversed both acute D2R antagonist (haloperidol)-induced catalepsy and chronic haloperidol-induced enkephalin mRNA expression. Together, these results demonstrate that A2ARs elicit behavioral and cellular responses despite either the genetic deficiency or pharmacological blockade of D2Rs. Thus, A2AR-mediated neural functions are partially independent of D2Rs. Moreover, endogenous adenosine acting at striatal A2ARs may be most accurately viewed as a facilitative modulator of striatal neuronal activity rather than simply as an inhibitory modulator of D2R neurotransmission.

A comparison of the potential role of the tetrodotoxin-insensitive sodium channels, PN3/SNS and NaN/SNS2, in rat models of chronic pain

Alterations in sodium channel expression and function have been suggested as a key molecular event underlying the abnormal processing of pain after peripheral nerve or tissue injury. Although the relative contribution of individual sodium channel subtypes to this process is unclear, the biophysical properties of the tetrodotoxin-resistant current, mediated, at least in part, by the sodium channel PN3 (SNS), suggests that it may play a specialized, pathophysiological role in the sustained, repetitive firing of the peripheral neuron after injury. Moreover, this hypothesis is supported by evidence demonstrating that selective “knock-down” of PN3 protein in the dorsal root ganglion with specific antisense oligodeoxynucleotides prevents hyperalgesia and allodynia caused by either chronic nerve or tissue injury. In contrast, knock-down of NaN/SNS2 protein, a sodium channel that may be a second possible candidate for the tetrodotoxin-resistant current, appears to have no effect on nerve injury-induced behavioral responses. These data suggest that relief from chronic inflammatory or neuropathic pain might be achieved by selective blockade or inhibition of PN3 expression. In light of the restricted distribution of PN3 to sensory neurons, such an approach might offer effective pain relief without a significant side-effect liability.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Effects of price changes on automobile driving: an exploration of alternative behavioral assumptions.

Mode of access: Internet.

Outer-space environment models for use with space vehicle simulators.

"Project no. 6114, task no. 60806. Prepared under Contract no. AF 33(616)-6858 by W. A. Livingston Jr. of the Cornell Aeronautical Laboratory, Inc."

Space-Time Smoothing Models for Surveillance and Complex Survey Data

Thesis (Ph.D.)--University of Washington, 2016-06

Hurt feelings in couple relationships: Towards integrative models of the negative effects of hurtful events

Previous research suggests that hurt feelings can have powerful effects on individual and relational outcomes. This study examined a typology of hurtful events in couple relationships, together with integrative models predicting ongoing effects on victims and relationships. Participants were 224 students from introductory and third-year psychology classes, who completed open-ended and structured measures concerning an event in which a partner had hurt their feelings. By tailoring Leary et al.'s (1998) typology to the context of romantic relationships, five categories of hurtful events were proposed: active disassociation, passive disassociation, criticism, infidelity, and deception. Analyses assessing similarities and differences among the categories confirmed the utility of the typology. Structural equation modeling showed that longer-term effects on the victim were predicted by relationship anxiety and by the victim's immediate reactions to the event (negative emotions and self-perceptions; feelings of rejection and powerlessness). In contrast, ongoing effects on the relationship were predicted by avoidance, the victim's attributions and perceptions of offender remorse, and the victim's own behavior. The results highlight the utility of an integrated approach to hurt, incorporating emotional, cognitive, and behavioral responses, and dimensions of attachment security.

The effect of service evaluations on behavioral intentions and quality of life

Understanding the contribution of marketing to economic and social outcomes is fundamental to broadening the focus of marketing. The authors develop a comprehensive model that integrates the impact of service quality and service satisfaction on both economic and societal outcomes. The model is validated using two random samples involving intensive health services. The results indicate that service quality and service satisfaction significantly enhance quality of life and behavioral intentions, highlighting that customer service has social as well as economic outcomes. This is an important finding given the movement toward recognizing social and environmental outcomes, such as emphasized through triple bottom-line reporting. The findings have important implications for managing service processes, for improving the quality of life of customers, and for enhancing customers' behavioral intentions toward the organization.