Azimuth

This is a book-length, creative nonfiction collection of essays with a critical introduction. These essays are illustrating the conflict of fitting within socially-formed identities. In theme, this collection explores class, gender, and sexuality of the self. Each section is introduced with a brief reflection which links the essays together

The Selfish Grandma Gene: The Roles of the X-Chromosome and Paternity Uncertainty in the Evolution of Grandmothering Behavior and Longevity

When considering inclusive fitness, it is expected that individuals will provide more care towards those with whom they are more closely related. Thus, if a selfish X-linked genetic element influenced care giving, we would expect care giving to vary with X-relatedness. Recent studies have shown that X-chromosome inheritance patterns may influence selection of traits affecting behavior and life-history. Sexually antagonistic (SA) zygotic drive could encourage individuals to help those with whom they are more likely to share genetic material at the expense of other relatives. We reanalyze previously reported data in light of this new idea. We also evaluate the effects of paternity uncertainty on SA-zygotic drive. Our evidence suggests that human paternal discrepancy is relatively low. Using published models, we find the effects of paternal discrepancy do not override opportunity for selection based on X-relatedness. Based on these results, longevity and grandmothering behaviors, including favoritism, may be more heavily influenced by selection on the X-chromosome than by paternity uncertainty

Comparison of osteoporosis pharmacotherapy fracture rates: Analysis of a marketScan® claims database cohort

Background: Several different classes of medications have been shown to be efficacious at preventing fractures in patients with osteoporosis. No study has compared real world efficacy at preventing fractures between all currently approved medications. Objectives: To directly compare the efficacy of all currently available osteoporosis medications by using a large population claims database. Methods: The Truven Health Analytics MarketScan® database from 2008 - 2012 was used to identify all patients who started a new osteoporosis medication. Patients who experienced a fracture after at least 12 months of treatment were identified and risk factors for fracture for all patients were recorded. Logistic regression was used to account for and quantify the contribution of risk factors, and to make direct comparisons between different osteoporosis medications. Results: A total of 51649 patients were included in the cohort, with an average age of 56 years. The overall incidence rate of fracture was 1.55 per 100 person - years of treatment. Orally administered medications had the lowest fracture rates, led by raloxifene and alendronate (1.24 and 1.54 respectively), while parenterally administered medications including teriparatide and zolerdonic acid had the highest rates (3.90 and 1.98 respectively). No statistically significant differences found between oral or parenterally administered bisphosphonate medications. Conclusions: While patients taking orally administered drugs including bisphosphonates had less frequent incident fracture no statistically significant differences were found between most drugs in head - to - head comparisons, even considering the route of administration of bisphosphonates. These findings support previous evidence that minimal differences in efficacy exist between different osteoporosis medications. This is the first study using a large database to compare all currently available osteoporosis treatments and will hopefully be augmented by further study to provide more evidence to make clinical decisions on osteoporosis medication use. © 2017 American Association of Neuropathologists, Inc

Physical activity and adolescent girls with ASD: effects of an individualized exercise program on cognitive, social and physical-health indicators

Physical activity, specifically aerobic exercise, has been associated with improved cognitive performance in both children and adults. Due to challenges with attention, motivation, and organization, adolescents with Autism Spectrum Disorder (ASD) may require unique supports and specific learning strategies in order to engage in recommended aerobic exercise practices and receive the associated health and cognitive benefits. Three adolescent girls with ASD participated in a 6-week individualized exercise program as part of a multiple-baseline single-case design study to explore (a) the effects of an individualized exercise plan on the duration of physical activity and amount of time participants engage in moderate-to-vigorous aerobic exercise, (b) the performance of participants on two executive function tasks -- visual processing speed and verbal working memory -- prior to and after moderate-to-vigorous aerobic activity, and (c) the strategies and supports necessary to facilitate and sustain safe and consistent aerobic activity for adolescents with ASD. The results indicate that visual supports and individualized work systems paired with the systematic monitoring of heart rate are effective tools to introduce and sustain moderate-to-vigorous aerobic physical activity. Participants reported increased interest in exercise and ability to carry out a customized exercise plan with minimal to no adult assistance.Doctor of Philosoph

Women’s Pregnancy Life History and Alzheimer’s Risk: Can Immunoregulation Explain the Link?

Background: Pregnancy is associated with improvement in immunoregulation that persists into the geriatric phase. Impaired immunoregulation is implicated in Alzheimer’s disease (AD) pathogenesis. Hence, we investigate the relationship between pregnancy and AD. Methods: Cross-sectional cohort of British women (N = 95). Cox proportional hazards modeling assessed the putative effects of cumulative months pregnant on AD risk and the mutually adjusted effects of counts of first and third trimesters on AD risk. Results: Cumulative number of months pregnant, was associated with lower AD risk (β = −1.90, exp(β) = 0.15, P = .02). Cumulative number of first trimesters was associated with lower AD risk after adjusting for third trimesters (β = −3.83, exp(β) = 0.02, P \u3c .01), while the latter predictor had no significant effect after adjusting for the former. Conclusions: Our observation that first trimesters (but not third trimesters) conferred protection against AD is more consistent with immunologic effects, which are driven by early gestation, than estrogenic exposures, which are greatest in late gestation. Results may justify future studies with immune biomarkers

Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data

An accurate representation of the voltage-dependent, time-varying energy consumption of end-use electric loads is essential for the operation of modern distribution automation (DA) schemes. Volt-var optimization (VVO), a DA scheme which can decrease energy consumption and peak demand, often leverages electric network models and power flow results to inform control decisions, making it sensitive to errors in load models. End-use load modeling can be improved with additional measurements from advanced metering infrastructure (AMI). This paper presents two novel machine learning algorithms for creating data-driven, time-varying load models for use with DA technologies such as VVO. The first algorithm uses AMI data, k-means clustering, and least-squares optimization to create predictive load models for individual electric customers. The second algorithm uses deep learning (via a convolution-based recurrent neural network) to incorporate additional data and increase model accuracy. The improved accuracy of the load models for both algorithms is validated through simulation

Phosphorylation of the actin binding protein Drebrin at S647 and is regulated by neuronal activity and PTEN

Defects in actin dynamics affect activity-dependent modulation of synaptic transmission and neuronal plasticity, and can cause cognitive impairment. A salient candidate actin-binding protein linking synaptic dysfunction to cognitive deficits is Drebrin (DBN). However, the specific mode of how DBN is regulated at the central synapse is largely unknown. In this study we identify and characterize the interaction of the PTEN tumor suppressor with DBN. Our results demonstrate that PTEN binds DBN and that this interaction results in the dephosphorylation of a site present in the DBN C-terminus - serine 647. PTEN and pS647-DBN segregate into distinct and complimentary compartments in neurons, supporting the idea that PTEN negatively regulates DBN phosphorylation at this site. We further demonstrate that neuronal activity increases phosphorylation of DBN at S647 in hippocampal neurons in vitro and in ex vivo hippocampus slices exhibiting seizure activity, potentially by inducing rapid dissociation of the PTEN:DBN complex. Our results identify a novel mechanism by which PTEN is required to maintain DBN phosphorylation at dynamic range and signifies an unusual regulation of an actin-binding protein linked to cognitive decline and degenerative conditions at the CNS synapse

DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability

Task and Motion Planning (TAMP) approaches are effective at planning long-horizon autonomous robot manipulation. However, because they require a planning model, it can be difficult to apply them to domains where the environment and its dynamics are not fully known. We propose to overcome these limitations by leveraging deep generative modeling, specifically diffusion models, to learn constraints and samplers that capture these difficult-to-engineer aspects of the planning model. These learned samplers are composed and combined within a TAMP solver in order to find action parameter values jointly that satisfy the constraints along a plan. To tractably make predictions for unseen objects in the environment, we define these samplers on low-dimensional learned latent embeddings of changing object state. We evaluate our approach in an articulated object manipulation domain and show how the combination of classical TAMP, generative learning, and latent embeddings enables long-horizon constraint-based reasoning