519 research outputs found

    Developing a Natural Language Processing Approach for Analyzing Student Ideas in Calculus-Based Introductory Physics

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    Research characterizing common student ideas about particular physics topics has made a significant impact on university-level physics teaching by providing knowledge that supports instructors to target their instruction and by informing curriculum development. This work utilizes a Natural Language Processing algorithm (Latent Dirichlet Allocation, or LDA) to categorize student ideas, with the goal of significantly expediting the process of categorizing student ideas. We preliminarily test the LDA approach by applying the algorithm to a collection of introductory physics student responses to a conceptual question about circuits, specifically attending to whether it is useful for characterizing conceptual resources, or student ideas that may be fruitful for science learning. We find that for a large enough collection of student responses (N ≈ 500), LDA can be useful for characterizing student resources for conceptual physics questions. We discuss some considerations that researchers may take into account as they interpret the results of the LDA algorithm for characterizing student’s physics ideas

    Gender Differences in the Combined Effects of Cardiovascular Disease and Osteoarthritis on Progression to Functional Impairment in Older Mexican Americans

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    Comorbidity (COM) is an important issue in aging. Cardiovascular disease (CVD) and osteoarthritis separately and together may modify the trajectories of functional decline. This analysis examines whether specific and unrelated COMs influence functional change differently and vary by gender

    Predicting kill sites of an apex predator from GPS data in different multi‐prey systems

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    Kill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires a correct identification of kill sites, often achieved by field-checking GPS location clusters (GLCs). However, there are potential sources of error included in kill-site identification, such as failing to detect GLCs that are kill sites, and misclassifying the generated GLCs (e.g., kill for nonkill) that were not field checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx (Lynx lynx), an apex predator of conservation concern in Europe, in three multiprey systems, with different combinations of wild, semidomestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affected the detection of GLC-indicated kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as nonkills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced from seven to three fixes per night without missing more than 5% of the ungulate kills, in a system composed of wild prey. Reducing the number of fixes per 24 h decreased the probability of detecting GLCs connected with kill sites, particularly those of semidomestic or domestic prey, and small prey. Random forest successfully predicted between 73%–90% of ungulate kills, but failed to classify most small prey in all systems, with sensitivity (true positive rate) lower than 65%. Additionally, removing domestic prey improved the algorithm’s overall accuracy. We provide a set of recommendations for studies focusing on kill-site detection that can be considered for other large carnivore species in addition to the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher

    Forestry and environmental conditions as determinants of pine marten Martes martes occurrence in Norway

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    The European pine marten Martes martes is often associated with late seral stage coniferous forest stands. Earlier research has indicated that this species may be negatively influenced by clearcutting practices. However, the effects of current clearcutting methods on pine marten occurrence in conjunction with changing environmental conditions are not well known. In this study, we combined four complete years of nationwide data collected during a long-term camera trap (CT) monitoring program in Norway. We employed a multi-scale occupancy model to investigate the relationship of pine marten occurrence to clearcuts (regenerating stands & LE; 10 years old) and forests & GE; 120 years old. We also examined pine marten detection in relation to habitat features (i.e. dominant microsite characteristics) and to varying snow depths and temperatures. We found no relationship between pine marten occurrence and the proportions of old forest and clearcuts at the landscape scale. At the habitat-patch scale, pine marten occurrence was positively associated with the presence of old forest patches and terrain ruggedness, but not with clearcuts & LE; 100 m from sites. At CT sites near clearcuts, the detection probability was negatively correlated with snow depth. In contrast, pine marten occurrence was positively associated with snow depth at CT sites > 100 m from clearcuts. Furthermore, the detection probability increased with temperature and the presence of boulders at CT sites. Boulders may provide important access points for foraging, and cover for resting and predator avoidance. While previous studies indicate that pine martens prefer older forest and avoid clearcuts, the current level and scale of clearcutting in Norway does not appear to influence its occurrence at the landscape scale

    Predicting Future Years of Life, Health, and Functional Ability: A Healthy Life Calculator for Older Adults

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    Introduction Planning for the future would be easier if we knew how long we will live and, more importantly, how many years we will be healthy and able to enjoy it. There are few well-documented aids for predicting our future health. We attempted to meet this need for persons 65 years of age and older. Methods Data came from the Cardiovascular Health Study, a large longitudinal study of older adults that began in 1990. Years of life (YOL) were defined by measuring time to death. Years of healthy life (YHL) were defined by an annual question about self-rated health, and years of able life (YABL) by questions about activities of daily living. Years of healthy and able life (YHABL) were the number of years the person was both Healthy and Able. We created prediction equations for YOL, YHL, YABL, and YHABL based on the demographic and health characteristics that best predicted outcomes. Internal and external validity were assessed. The resulting CHS Healthy Life Calculator (CHSHLC) was created and underwent three waves of beta testing. Findings A regression equation based on 11 variables accounted for about 40% of the variability for each outcome. Internal validity was excellent, and external validity was satisfactory. As an example, a very healthy 70-year-old woman might expect an additional 20 YOL, 16.8 YHL, 16.5 YABL, and 14.2 YHABL. The CHSHLC also provides the percent in the sample who differed by more than 5 years from the estimate, to remind the user of variability. Discussion The CHSHLC is currently the only available calculator for YHL, YABL, and YHABL. It may have limitations if today’s users have better prospects for health than persons in 1990. But the external validity results were encouraging. The remaining variability is substantial, but this is one of the few calculators that describes the possible accuracy of the estimates. Conclusion The CHSHLC, currently at http://diehr.com/paula/healthspan, meets the need for a straightforward and well-documented estimate of future years of healthy and able life that older adults can use in planning for the future

    The Association of Intensive Blood Pressure Treatment and Non-Fatal Cardiovascular or Serious Adverse Events in Older Adults With Mortality: Mediation Analysis in Sprint

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    AIMS: Randomized clinical trials of hypertension treatment intensity evaluate the effects on incident major adverse cardiovascular events (MACEs) and serious adverse events (SAEs). Occurrences after a non-fatal index event have not been rigorously evaluated. The aim of this study was to evaluate the association of intensive (\u3c120 \u3emmHg) to standard (\u3c140 \u3emmHg) blood pressure (BP) treatment with mortality mediated through a non-fatal MACE or non-fatal SAE in 9361 participants in the Systolic Blood Pressure Intervention Trial. METHODS AND RESULTS: Logistic regression and causal mediation modelling to obtain direct and mediated effects of intensive BP treatment. Primary outcome was all-cause mortality (ACM). Secondary outcomes were cardiovascular (CVM) and non-CV mortality (non-CVM). The direct effect of intensive treatment was a lowering of ACM [odds ratio (OR) 0.75, 95% confidence interval (CI): 0.60-0.94]. The MACE-mediated effect substantially attenuated (OR 0.96, 95% CI: 0.92-0.99) ACM, while the SAE-mediated effect was associated with increased (OR 1.03, 95% CI: 1.01-1.05) ACM. Similar patterns were noted for intensive BP treatment on CVM and non-CVM. We also noted that SAE incidence was 3.9-fold higher than MACE incidence (13.7 vs. 3.5%), and there were a total of 365 (3.9%) ACM cases, with non-CVM being 2.6-fold higher than CVM [2.81% (263/9361) vs. 1.09% (102/9361)]. The SAE to MACE and non-CVM to CVM preponderance was found across all age groups, with the ≥80-year age group having the highest differences. CONCLUSION: The current analytic techniques demonstrated that intensive BP treatment was associated with an attenuated mortality benefit when it was MACE-mediated and possibly harmful when it was SAE-mediated. Current cardiovascular trial reporting of treatment effects does not allow expansion of the lens to focus on important occurrences after the index event
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