Exploring the role of therapy process and outcome in interventions that target adolescent identity and intimacy

This study examined the feasibility of using a session impact measure with a sample of 24 at risk high school students participating in an intervention targeting identity and intimacy. Three therapists led 3 intervention groups with the same format. The study investigated the impact of therapy process, including Group, Facilitator, Skills, and Exploration impacts as measured by the Session Evaluation Form (SEF). The study also investigated the differential impact of session process on intervention outcome as measured by the CPSS, EPSI, RAVS, EIPQ and Youth Report Form. Analyses were conducted using descriptive statistics, frequencies, one-way analysis of variance (ANOVA), and Chi square tests. The results supported the utility of the SEF and they tentatively supported the impact of the therapist on participants' perceptions of therapeutic processes and on intervention outcome. In particular, Group 1 performed better than Group 3. This study found that the SEF is a useful session impact measure.

Investigation of particle identification methods for the analysis of the photoproduction of neutral kaons on a liquid deuterium target

The Photoproduction of neutral kaons off a deuteron target has been investigated at the Tohoku University Laboratory of Nuclear Science. The PID methods investigated incorporated a combination of momentum, velocity (β=v/c), and energy deposition per unit length (dE/dx) measurements. The analysis demonstrates that energy deposition and time of flight are exceedingly useful. A higher signal to background ratio was achieved for hard cuts in combination. A probabilistic likelihood estimation approach (LE) as a method for PID was also explored. The probability of a particle being correctly identified by this LE method and the preliminary results denote the need for highly precise limitations on the distributions from which the parameters would be extracted. It was confirmed that these PID are applicable approaches to properly identify pions for the analysis of this experiment. However, the background evident in the mass spectra points to the need for a higher level of proton identification.

Energy-Efficient Self-Organization of Wireless Acoustic Sensor Networks for Ground Target Tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

A Sting in the Tail : The N-Terminal Domain of the Androgen Receptor as a Drug Target

AEM was supported by a BBSRC-CASE studentship award. Research in the IJM laboratory is currently supported by the Chief Scientist's Office of the Scottish Government and the charity Friends of Anchor.

Parametric, Optimization-Based Study on the Feasibility of a Multisegment Antisolvent Crystallizer for in Situ Fines Removal and Matching of Target Size Distribution

Peer reviewed

Reducing emissions from agriculture to meet the 2°C target

Peer reviewed

Vulnerable children, stigmatised smokers : The social construction of target audiences in media debates on policies regulating smoking in vehicles

Acknowledgements S.H., S.S. and S.D. developed the study concept and gained funding for the work. S.H. developed the study design. J.B. and H.W. drafted the manuscript. J.B. and H.W. developed the coding frame and coded the articles. S.H., S.S. and S.D. critically revised the manuscript. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded by Cancer Research UK (C47682/A16930) and the Scottish School of Public Health Research. Sheila Duffy is Chief Executive of ASH Scotland. Heide Weishaar and Shona Hilton are funded by the UK Medical Research Council as part of the Informing Healthly Public Policy programme (MC_UU12017-15) at the MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. The authors declare no additional conflicting interest.

Development and characterization of long-circulating emulsions to target solid tumors

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Rho-kinase as a therapeutic target in vascular diseases:striking nitric oxide signaling

Rho GTPases are a globular, monomeric group of small signaling G-protein molecules. Rho-associated protein kinase/Rho-kinase (ROCK) is a downstream effector protein of the Rho GTPase. Rho-kinases are the potential therapeutic targets in the treatment of cardiovascular diseases. Here, we have primarily discussed the intriguing roles of ROCK in cardiovascular health in relation to nitric oxide signaling. Further, we highlighted the biphasic effects of Y-27632, a ROCK inhibitor under shear stress, which acts as an agonist of nitric oxide production in endothelial cells. The biphasic effects of this inhibitor raised the question of safety of the drug usage in treating cardiovascular diseases.

Design of Antibacterial Prochelators to Target Drug-Resistant Bacteria

Transition metals such as iron and copper are valued in biology for their redox activities because they are able to access various oxidation states. However, these transition metals are also implicated in a number of human disease states and play a role in bacterial infections. The ability to manipulate and monitor metal ions has vast implications on the fields of biology and human health. As such, the research described here covers two related goals: to manipulate metals in specific biological circumstances and to visualize this disturbance in cellular metal homeostasis.

Antibiotic resistance necessitates the development of drugs that exploit new mechanisms of action such as the disruption of metal homeostasis. In order to manipulate metals at the site of bacterial infection, two prochelators were developed around a β-lactam core such that the active chelator is released in the presence of bacteria that produce the resistance-causing β-lactamase enzyme. Both prochelators display enhanced activity toward resistant bacteria compared to clinical antibiotics.

Fluorescent sensors are a powerful tool for detecting small concentrations of biological analytes. Two analogs of a ratiometric fluorescent sensor were designed and synthesized to monitor cellular concentrations of copper and iron. These sensors were found to operate as designed in vitro; however the fluorescence intensity necessary for quantification of cellular metal pools has not yet been achieved.

Grabbing Your Attention: The Impact of Finding a First Target in Multiple-Target Search

For over 50 years, the Satisfaction of Search effect, and more recently known as the Subsequent Search Miss (SSM) effect, has plagued the field of radiology. Defined as a decrease in additional target accuracy after detecting a prior target in a visual search, SSM errors are known to underlie both real-world search errors (e.g., a radiologist is more likely to miss a tumor if a different tumor was previously detected) and more simplified, lab-based search errors (e.g., an observer is more likely to miss a target ‘T’ if a different target ‘T’ was previously detected). Unfortunately, little was known about this phenomenon’s cognitive underpinnings and SSM errors have proven difficult to eliminate. However, more recently, experimental research has provided evidence for three different theories of SSM errors: the Satisfaction account, the Perceptual Set account, and the Resource Depletion account. A series of studies examined performance in a multiple-target visual search and aimed to provide support for the Resource Depletion account—a first target consumes cognitive resources leaving less available to process additional targets.

To assess a potential mechanism underlying SSM errors, eye movements were recorded in a multiple-target visual search and were used to explore whether a first target may result in an immediate decrease in second-target accuracy, which is known as an attentional blink. To determine whether other known attentional distractions amplified the effects of finding a first target has on second-target detection, distractors within the immediate vicinity of the targets (i.e., clutter) were measured and compared to accuracy for a second target. To better understand which characteristics of attention were impacted by detecting a first target, individual differences within four characteristics of attention were compared to second-target misses in a multiple-target visual search.

The results demonstrated that an attentional blink underlies SSM errors with a decrease in second-target accuracy from 135ms-405ms after detection or re-fixating a first target. The effects of clutter were exacerbated after finding a first target causing a greater decrease in second-target accuracy as clutter increased around a second-target. The attentional characteristics of modulation and vigilance were correlated with second- target misses and suggest that worse attentional modulation and vigilance are predictive of more second-target misses. Taken together, these result are used as the foundation to support a new theory of SSM errors, the Flux Capacitor theory. The Flux Capacitor theory predicts that once a target is found, it is maintained as an attentional template in working memory, which consumes attentional resources that could otherwise be used to detect additional targets. This theory not only proposes why attentional resources are consumed by a first target, but encompasses the research in support of all three SSM theories in an effort to establish a grand, unified theory of SSM errors.

Sensor Planning for Bayesian Nonparametric Target Modeling

Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including environmental monitoring, traffic planning, endangered species tracking, dynamic scene analysis, autonomous robot navigation, and human motion modeling. As shown by these successful applications, Bayesian nonparametric models are able to adjust their complexities adaptively from data as necessary, and are resistant to overfitting or underfitting. However, most existing works assume that the sensor measurements used to learn the Bayesian nonparametric target kinematics models are obtained a priori or that the target kinematics can be measured by the sensor at any given time throughout the task. Little work has been done for controlling the sensor with bounded field of view to obtain measurements of mobile targets that are most informative for reducing the uncertainty of the Bayesian nonparametric models. To present the systematic sensor planning approach to leaning Bayesian nonparametric models, the Gaussian process target kinematics model is introduced at first, which is capable of describing time-invariant spatial phenomena, such as ocean currents, temperature distributions and wind velocity fields. The Dirichlet process-Gaussian process target kinematics model is subsequently discussed for modeling mixture of mobile targets, such as pedestrian motion patterns.

Novel information theoretic functions are developed for these introduced Bayesian nonparametric target kinematics models to represent the expected utility of measurements as a function of sensor control inputs and random environmental variables. A Gaussian process expected Kullback Leibler divergence is developed as the expectation of the KL divergence between the current (prior) and posterior Gaussian process target kinematics models with respect to the future measurements. Then, this approach is extended to develop a new information value function that can be used to estimate target kinematics described by a Dirichlet process-Gaussian process mixture model. A theorem is proposed that shows the novel information theoretic functions are bounded. Based on this theorem, efficient estimators of the new information theoretic functions are designed, which are proved to be unbiased with the variance of the resultant approximation error decreasing linearly as the number of samples increases. Computational complexities for optimizing the novel information theoretic functions under sensor dynamics constraints are studied, and are proved to be NP-hard. A cumulative lower bound is then proposed to reduce the computational complexity to polynomial time.

Three sensor planning algorithms are developed according to the assumptions on the target kinematics and the sensor dynamics. For problems where the control space of the sensor is discrete, a greedy algorithm is proposed. The efficiency of the greedy algorithm is demonstrated by a numerical experiment with data of ocean currents obtained by moored buoys. A sweep line algorithm is developed for applications where the sensor control space is continuous and unconstrained. Synthetic simulations as well as physical experiments with ground robots and a surveillance camera are conducted to evaluate the performance of the sweep line algorithm. Moreover, a lexicographic algorithm is designed based on the cumulative lower bound of the novel information theoretic functions, for the scenario where the sensor dynamics are constrained. Numerical experiments with real data collected from indoor pedestrians by a commercial pan-tilt camera are performed to examine the lexicographic algorithm. Results from both the numerical simulations and the physical experiments show that the three sensor planning algorithms proposed in this dissertation based on the novel information theoretic functions are superior at learning the target kinematics with

little or no prior knowledge

Development and characterization of long-circulating emulsions to target solid tumors

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.