Rote-LCS learning classifier system for classification and prediction

Machine Learning (ML) involves the use of computer algorithms to solve for approximate solutions to problems with large, complex search spaces. Such problems have no known solution method, and search spaces too large to allow brute force search to be feasible. Evolutionary algorithms (EA) are a subset of machine learning algorithms which simulate fundamental concepts of evolution. EAs do not guarantee a perfect solution, but rather facilitate convergence to a solution of which the accuracy depends on a given EA\u27s learning architecture and the dynamics of the problem. Learning classifier systems (LCS) are algorithms comprising a subset of EAs. The Rote-LCS is a novel Pittsburgh-style LCS for supervised learning problems. The Rote models a solution space as a hyper-rectangle, where each independent variable represents a dimension. Rote rules are formed by binary trees with logical operators (decision trees) with relational hypotheses comprising the terminal nodes. In this representation, sub-rules (minor-hypotheses) are partitions on hyper-planes, and rules (major-hypotheses) are multidimensional partitions. The Rote-LCS has exhibited very high accuracy on classification problems, particularly Boolean problems, thus far. The Rote-LCS offers an additional attribute uncommon among machine learning algorithms - human readable solutions. Despite representing a multidimensional search space, Rote solutions may be graphed as two-dimensional trees. This makes the Rote-LCS a good candidate for supervised classification problems where insight is needed into the dynamics of a problem. Solutions generated by Rote-LCS could prospectively be used by scientists to form hypotheses regarding interactions between independent variables of a given problem. --Abstract, page iv

Testing a Brief Treatment to Reduce the Frequency of Panic Attacks in a Clinical Outpatient Population

Panic attacks, the key symptom of panic disorder and an associated feature of various anxiety disorders, are extremely distressing events that can negatively impact an individual’s mental health, physical health, and quality of life. This study validated a brief treatment for panic attacks, designed to reduce the frequency of panic attacks after the first session, in an outpatient clinical population. One participant was recruited to participate in this single case experimental ABA design with follow-up, where a reversal was not expected, due to the maintenance of positive effects. The treatment included both cognitive and behavioral techniques. The results were analyzed using simulation modeling analysis, as well as visual analysis. This treatment produced clinically significant effects by reducing the frequency and severity of panic attacks, reducing symptoms of anxiety and panic, decreasing the frequency of cognitive distortions, and increasing the level of functioning. Additionally, these gains were maintained at a 3- month follow-up. It is hoped that this intervention can help clinicians treat panic disorder and improve their effectiveness and efficiency by reducing the time needed to significantly decrease panic attacks. It is also hoped that this intervention might be expanded for use with other panic-related anxiety disorders. Finally, it is possible that this study will encourage efforts toward briefer treatments for other disorders

State Regulatory Competition and the Threat to Corporate Governance

The subject of this paper is the impact of the new globalized order on the integrity of corporate governance. Corporate governance is the system of laws, markets and institutions that seeks to control and discipline corporate activity in the service of the public interest. Over the last several years, many critics have bemoaned the growing integration of various economic markets across national boundaries because it is seen to lessen the capacity of states to regulate corporate behaviour. Essentially, the claim is that in a setting of reduced barriers to factor and product mobility, corporations are rendered much more effective in their capacity to extract regulatory concessions from host governments, and these concessions have the effect of lowering social welfare. The argument is that in a setting of high international corporate mobility, footloose corporations will relocate their operations to whichever jurisdiction offers the most congenial (meaning least stringent) regulation. In the face of certain corporate migration in response to more stringent regulation, states will have no choice but to refrain from adopting socially optimal regulation. This is because states fear the loss of benefits associated with corporate activity: namely, employment, investment and tax revenue. The effect is an international race to the bottom in which states are rendered helpless in countering the effect of heightened corporate mobility

Method for machining holes in composite materials

A method for boring well defined holes in a composite material such as graphite/epoxy is discussed. A slurry of silicon carbide powder and water is projected onto a work area of the composite material in which a hole is to be bored with a conventional drill bit. The silicon carbide powder and water slurry allow the drill bit, while experiencing only normal wear, to bore smooth, cylindrical holes in the composite material

Effects of Climate Nonstationarity on Low-Flow Models for Southern New England

Thesis advisor: Noah SnyderIncreasing attention has been drawn to the need for reliable streamflow estimates at ungaged locations under a range of climatic and hydrologic conditions. Climate projections for the northeastern United States over the 21st century--which include significant increases in temperature and precipitation--could have broad impacts on streamflows, potentially reducing the accuracies of existing streamflow models for the region. This thesis investigates recent changes in daily flow-durations in southern New England, and examines their influence on the reliability of the low-flow models for Massachusetts presented by Ries and Friesz (2000). An analysis of discharge data collected at gaging sites through water year 2012 revealed increases in nearly all flow durations at sites across southern New England since the mid-20th century, whereas very low flows (quantiles at or above the 95-percent exceedance probability) generally showed decreases, especially since the 1990s. Twenty-year moving streamflow quantiles at each of ten selected exceedance probabilities were examined for the periods of record of 16 streamflow-gaging stations in southern New England. The beginning of water year 1992 appeared to mark an inflection point in low-flow quantiles, before which very low flows were steady or increasing, and after which these flows showed near-universal decreases. While the observed peak in 20-year low-flow quantiles around 1992 may be due to the statistical method used to calculate the quantile trends, the inflection point could also be an indicator of when increasing evapotranspiration surpassed increasing precipitation as the principal climatic driver of changes in low flows in southern New England. The general upward translation of the flow-duration curve observed over the last 60 years is very likely linked to increases in annual precipitation during this period, while the decreases in very low flows are likely due to changes in climatic variables (increasing summer temperatures and evapotranspiration rates), and amplified by anthropogenic factors (greater areas of impervious surfaces and increasing rates of surface- and ground-water withdrawal). The data suggest that increasing precipitation rates have already caused the Ries and Friesz (2000) equations for the median low flows (Q50 to Q75) to become biased towards underestimation, and decreases in very low flows threaten to render the models for these flows biased towards overestimation in the coming decades. The streamflow quantile trends (for both the entire period of record of the gaging stations and just the post-1992 period) for each of the ten flow-durations of interest were extended into the future to the point where the corresponding Ries and Friesz (2000) model would fail (when actual flow durations would be outside the 90-percent prediction intervals for the estimated flows for greater than 10% of sites). The models for the lowest streamflows are estimated to lose validity by as early as 2018. Climate change is predicted to have significant effects on streamflow characteristics in southern New England over the 21st century, and the results of this study indicate that the Ries and Freisz (2000) low-flow models should be reformulated using more recent streamflow data within the next decade, and validated every 20 years thereafter to ensure their accuracies are maintained despite the effects of regional nonstationarity.Thesis (MS) — Boston College, 2014.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Earth and Environmental Sciences

Adherence to prescribing restrictions for HER2-positive metastatic breast cancer in Australia: A national population-based observational study (2001-2016)

Background: Targeted cancer therapy is often complex, involving multiple agents and chemotherapeutic partners. In Australia, prescribing restrictions are put in place to reflect existing evidence of cost-effectiveness of these medicines. As therapeutic options continue to expand, these restrictions may not be perceived to align with best practice and it is not known if their use in the real-world clinic adheres to these restrictions. We examined the treatment of women receiving trastuzumab for HER2-positive metastatic breast cancer (HER2+MBC) to determine the extent to which treatment adhered to national prescribing restrictions. Patients and methods: Our population-based, retrospective cohort study used dispensing records for every Australian woman initiating publicly-subsidised trastuzumab for HER2+MBC between 2001±2013, followed through 2016. We used group-based trajectory models (GBTMs) to cluster patients, first on their patterns of trastuzumab exposure, and then on their patterns of lapatinib and chemotherapy exposure. We described the characteristics of patients within each cluster, and examined their treatments and combinations of treatments to determine restriction adherence. Results: Of 5,052 patients initiating trastuzumab, 1,795 (36%) received at least one non-adherent HER2-targeted treatment. The most common non-adherent treatments were trastuzumab combinations involving vinorelbine (24% of non-adherent treatments); capecitabine (24%); and anthracyclines (10%). Non-adherent lapatinib use was observed in 4% of patients. GBTM identified three trastuzumab exposure clusters, each containing three further subclusters. The largest proportions of non-adherent treatments were in sub-clusters with longer trastuzumab exposure and more non-taxane chemotherapy. Patients in these sub-clusters were younger than those in sub-clusters with less non-adherent treatment. Conclusions: Our study highlights that, even during the relatively simpler treatment era of our study period, a substantial amount of treatment did not adhere to prescribing restrictions. As more trials are conducted exploring pertuzumab and T-DM1 in combination with different chemotherapies and other HER2-targeted therapies, the regulation and funding of HER2-targeted treatment will become more challenging

Going the distance for protein function prediction: a new distance metric for protein interaction networks

Due to an error introduced in the production process, the x-axes in the first panels of Figure 1 and Figure 7 are not formatted correctly. The correct Figure 1 can be viewed here: http://dx.doi.org/10.1371/annotation/343bf260-f6ff-48a2-93b2-3cc79af518a9In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.MC, HZ, NMD and LJC were supported in part by National Institutes of Health (NIH) R01 grant GM080330. JP was supported in part by NIH grant R01 HD058880. This material is based upon work supported by the National Science Foundation under grant numbers CNS-0905565, CNS-1018266, CNS-1012910, and CNS-1117039, and supported by the Army Research Office under grant W911NF-11-1-0227 (to MEC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript