The Role of Plant Litter in Driving Plant-Soil Feedbacks

Most studies focusing on plant-soil feedbacks (PSFs) have considered direct interactions between plants, abiotic conditions (e. g., soil nutrients) and rhizosphere communities (e.g., pathogens, mutualists). However, few studies have addressed the role of indirect interactions mediated by plant litter inputs. This is problematic because it has left a major gap in our understanding of PSFs in natural ecosystems, where plant litter is a key component of feedback effects. Here, we propose a new conceptual framework that integrates rhizosphere- and litter-mediated PSF effects. Our framework provides insights into the relative contribution of direct effects mediated by interactions between plants and soil rhizosphere organisms, and indirect effects between plants and decomposer organisms mediated by plant root and shoot litter. We distinguish between three pathways through which senesced root and shoot litter may influence PSFs. Specifically, we examine: (1) physical effects of litter (layer) traits on seed germination, soil structure, and plant growth; (2) chemical effects of litter on concentrations of soil nutrients and secondary metabolites (e.g., allelopathic chemicals); and (3) biotic effects of saprotrophic soil communities that can perform different functional roles in the soil food web, or that may have specialized interactions with litter types, thereby altering soil nutrient cycling. We assess the role of litter in PSF effects via physical, chemical and biotic pathways to address how litter-mediated feedbacks may play out relative to, and in interaction with, feedbacks mediated through the plant rhizosphere. We also present one of the first experimental studies to show the occurrence and species-specificity of litter-mediated feedbacks and we identify critical research gaps. By formally incorporating the plant-litter feedback pathway into PSF experiments, we will further our understanding of PSFs under natural conditions

Methods of exposure assessment: lead-contaminated dust in Philadelphia schools.

This study was conducted to develop a method that would accurately assess children's exposure to lead in schools in Philadelphia, Pennsylvania. We examined three wipe sample protocols: one included accessible surfaces such as desktops and windowsills, the second included inaccessible surfaces such as the top of filing cabinets and light fixtures, and the third included hand wipes of the study participants. Surface wipes were collected at 10 locations from accessible and inaccessible classroom surfaces (n = 11 at each location) and from the palms of student subjects in the same locations (n = 168). We found a significant difference in lead dust concentrations determined by the three protocols (F = 4.619; 2,27 degrees of freedom; p = 0.019). Lead dust concentrations were significantly elevated at the inaccessible surfaces yet they were uniformly low on the accessible surfaces and the children's palms. These findings were consistent with observed changes in blood lead levels of study participants: after 6 months of exposure to the study locations, 156 of 168 children experienced no change in blood lead level, whereas 12 experienced only a minimal change of 1-2 microg/dL. The mere presence of lead in inaccessible dust in the school environment does not automatically constitute a health hazard because there may not be a completed exposure pathway

Interpretable prediction of necrotizing enterocolitis from machine learning analysis of premature infant stool microbiota

Background Necrotizing enterocolitis (NEC) is a common, potentially catastrophic intestinal disease among very low birthweight premature infants. Affecting up to 15% of neonates born weighing less than 1500 g, NEC causes sudden-onset, progressive intestinal inflammation and necrosis, which can lead to significant bowel loss, multi-organ injury, or death. No unifying cause of NEC has been identified, nor is there any reliable biomarker that indicates an individual patient’s risk of the disease. Without a way to predict NEC in advance, the current medical strategy involves close clinical monitoring in an effort to treat babies with NEC as quickly as possible before irrecoverable intestinal damage occurs. In this report, we describe a novel machine learning application for generating dynamic, individualized NEC risk scores based on intestinal microbiota data, which can be determined from sequencing bacterial DNA from otherwise discarded infant stool. A central insight that differentiates our work from past efforts was the recognition that disease prediction from stool microbiota represents a specific subtype of machine learning problem known as multiple instance learning (MIL). Results We used a neural network-based MIL architecture, which we tested on independent datasets from two cohorts encompassing 3595 stool samples from 261 at-risk infants. Our report also introduces a new concept called the “growing bag” analysis, which applies MIL over time, allowing incorporation of past data into each new risk calculation. This approach allowed early, accurate NEC prediction, with a mean sensitivity of 86% and specificity of 90%. True-positive NEC predictions occurred an average of 8 days before disease onset. We also demonstrate that an attention-gated mechanism incorporated into our MIL algorithm permits interpretation of NEC risk, identifying several bacterial taxa that past work has associated with NEC, and potentially pointing the way toward new hypotheses about NEC pathogenesis. Our system is flexible, accepting microbiota data generated from targeted 16S or “shotgun” whole-genome DNA sequencing. It performs well in the setting of common, potentially confounding preterm neonatal clinical events such as perinatal cardiopulmonary depression, antibiotic administration, feeding disruptions, or transitions between breast feeding and formula. Conclusions We have developed and validated a robust MIL-based system for NEC prediction from harmlessly collected premature infant stool. While this system was developed for NEC prediction, our MIL approach may also be applicable to other diseases characterized by changes in the human microbiota

Essays in Applied Economics

Essay 1 studies physician agency problems, which arise whenever physicians fail to maximize their patients\u27 preferences, given available information. These agency problems are well documented, but the magnitude of their welfare consequences for patients---the losses from suboptimal treatment choice induced by agency---are unclear. I infer patient drug preference from their compliance decisions. I begin by showing that initial prescriptions respond to physician financial incentives to control costs and to pharmaceutical detailing, but compliance does not, pointing to agency problems. I then develop and estimate a model of physician-patient interactions where physician write initial prescriptions, but patients choose whether to comply. Fully eliminating agency problems increases compliance by 6.5 percentage points, and raises patient welfare by 22\% of drug spending. Contracts that better align doctor and patient preferences can improve patient welfare, but attain only half the gains from eliminating agency completely. Although physician agency problems reduce patient welfare, eliminating them is thus likely difficult. Essay 2, co-authored with Alexander M. Gelber and Damon Jones, studies frictions in adjusting earnings to changes in the Social Security Annual Earnings Test (AET) using a panel of Social Security Administration microdata on one percent of the U.S. population from 1961 to 2006. Individuals continue to bunch at the convex kink the AET creates even when they are no longer subject to the AET, consistent with the existence of earnings adjustment frictions in the U.S. We develop a novel estimation framework and estimate in a baseline case that the earnings elasticity with respect to the implicit net-of-tax share is 0.23, and the fixed cost of adjustment is \$152.08. Essay 3 studies the impact of health expenditure risk on annuitization. Theoretical research suggests that such risk can have an ambiguous influence on the annuitization decisions of the elderly. I provide empirical evidence on this linkage, by estimating the impact of supplemental Medicare insurance (Medigap) coverage on the annuity demand of older Americans. Medigap coverage has a strong impact on annuitization: the extensive margin elasticity is 0.39, the overall elasticity of private annuity income with respect to Medigap coverage is 0.56. These results are robust to controls for health, wealth, and preferences, as well as other robustness tests. They suggest that medical expenditure risk has a large impact on underannuitization

Age-related changes in matching novel objects across viewpoints

Peer reviewedPreprin

Air pollution exposure estimation using dispersion modelling and continuous monitoring data in a prospective birth cohort study in the Netherlands

Previous studies suggest that pregnant women and children are particularly vulnerable to the adverse effects of air pollution. A prospective cohort study in pregnant women and their children enables identification of the specific effects and critical periods. This paper describes the design of air pollution exposure assessment for participants of the Generation R Study, a population-based prospective cohort study from early pregnancy onwards in 9778 women in the Netherlands. Individual exposures to PM10 and NO2 levels at the home address were estimated for mothers and children, using a combination of advanced dispersion modelling and continuous monitoring data, taking into account the spatial and temporal variation in air pollution concentrations. Full residential history was considered. We observed substantial spatial and temporal variation in air pollution exposure levels. The Generation R Study provides unique possibilities to examine effects of short- and long-term air pollution exposure on various maternal and childhood outcomes and to identify potential critical windows of exposure

When, where and how osteoporosis-associated fractures occur: An analysis from the global longitudinal study of osteoporosis in women (GLOW)

Objective: To examine when, where and how fractures occur in postmenopausal women. Methods: We analyzed data from the Global Longitudinal Study of Osteoporosis in Women (GLOW), including women aged ≥55 years from the United States of America, Canada, Australia and seven European countries. Women completed questionnaires including fracture data at baseline and years 1, 2 and 3. Results: Among 60,393 postmenopausal women, 4122 incident fractures were reported (86% non-hip, non-vertebral [NHNV], 8% presumably clinical vertebral and 6% hip). Hip fractures were more likely to occur in spring, with little seasonal variation for NHNV or spine fractures. Hip fractures occurred equally inside or outside the home, whereas 65% of NHNV fractures occurred outside and 61% of vertebral fractures occurred inside the home. Falls preceded 68-86% of NHNV and 68-83% of hip fractures among women aged ≤64 to ≥85 years, increasing with age. About 45% of vertebral fractures were associated with falls in all age groups except those ≥85 years, when only 24% occurred after falling. Conclusion: In this multi-national cohort, fractures occurred throughout the year, with only hip fracture having a seasonal variation, with a higher proportion in spring. Hip fractures occurred equally within and outside the home, spine fractures more often in the home, and NHNV fractures outside the home. Falls were a proximate cause of most hip and NHNV fractures. Postmenopausal women at risk for fracture need counseling about reducing potentially modifiable fracture risk factors, particularly falls both inside and outside the home and during all seasons of the year. © 2013 Costa et al