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  1. Abstract

    Extant research on the gender pay gap suggests that men and women who do the same work for the same employer receive similar pay, so that processes sorting people into jobs are thought to account for the vast majority of the pay gap. Data that can identify women and men who do the same work for the same employer are rare, and research informing this crucial aspect of gender differences in pay is several decades old and from a limited number of countries. Here, using recent linked employer–employee data from 15 countries, we show that the processes sorting people into different jobs account for substantially less of the gender pay differences than was previously believed and that within-job pay differences remain consequential.

  2. ABSTRACT

    The study of food consumption, diet, and related concepts is motivated by diverse goals, including understanding why food consumption impacts our health, and why we eat the foods we do. These varied motivations can make it challenging to define and measure consumption, as it can be specified across nearly infinite dimensions—from micronutrients to carbon footprint to food preparation. This challenge is amplified by the dynamic nature of food consumption processes, with the underlying phenomena of interest often based on the nature of repeated interactions with food occurring over time. This complexity underscores a need to not only improve how we measure food consumption but is also a call to support theoreticians in better specifying what, how, and why food consumption occurs as part of processes, as a prerequisite step to rigorous measurement. The purpose of this Perspective article is to offer a framework, the consumption process framework, as a tool that researchers in a theoretician role can use to support these more robust definitions of consumption processes. In doing so, the framework invites theoreticians to be a bridge between practitioners who wish to measure various aspects of food consumption and methodologists who can develop measurement protocols and technologies thatmore »can support measurement when consumption processes are clearly defined. In the paper we justify the need for such a framework, introduce the consumption process framework, illustrate the framework via a use case, and discuss existing technologies that enable the use of this framework and, by extension, more rigorous study of consumption. This consumption process framework demonstrates how theoreticians could fundamentally shift how food consumption is defined and measured towards more rigorous study of what, how, and why food is eaten as part of dynamic processes and a deeper understanding of linkages between behavior, food, and health.

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  3. During the last few decades, the interest over chalcopyrite and related photovoltaics has been growing due the outstanding structural and electrical properties of the thin-film Cu(In,Ga)Se2 photoabsorber. More recently, thin film deposition through solution processing has gained increasing attention from the industry, due to the potential low-cost and high-throughput production. To this end, the elimination of the selenization procedure in the synthesis of Cu(In,Ga)Se2 nanoparticles with following dispersion into ink formulations for printing/coating deposition processes are of high relevance. However, most of the reported syntheses procedures give access to tetragonal chalcopyrite Cu(In,Ga)Se2 nanoparticles, whereas methods to obtain other structures are scarce. Herein, we report a large-scale synthesis of high-quality Cu(In,Ga)Se2 nanoparticles with wurtzite hexagonal structure, with sizes of 10–70 nm, wide absorption in visible to near-infrared regions, and [Cu]/[In + Ga] ≈ 0.8 and [Ga]/[Ga + In] ≈ 0.3 metal ratios. The inclusion of the synthesized NPs into a water-based ink formulation for screen printing deposition results in thin films with homogenous thickness of ≈4.5 µm, paving the way towards environmentally friendly roll-to-roll production of photovoltaic systems.
  4. Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG 100cpm ) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e.,  z scores) and unstandardized SB metrics. Mean errors (AP − AG 100cpm ) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG 100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five ofmore »24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG 100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG 100cpm measures and similar standardized associations with health outcomes suggest that studies using AG 100cpm are useful, though not ideal, for studying SB in older adults.« less
  5. Background : Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method : Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results : The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression andmore »76% for random forest), even without performing any feature engineering. Conclusion : Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.« less
  6. Abstract

    Type A γ-aminobutyric acid receptors (GABAARs) are inhibitory pentameric ligand-gated ion channels in the brain. Many anesthetics and neurosteroids act through binding to the GABAAR transmembrane domain (TMD), but the structural basis of their actions is not well understood and no resting-state GABAAR structure has been determined. Here, we report crystal structures of apo and the neurosteroid anesthetic alphaxalone-bound desensitized chimeric α1GABAAR (ELIC-α1GABAAR). The chimera retains the functional and pharmacological properties of GABAARs, including potentiation, activation and desensitization by alphaxalone. The apo-state structure reveals an unconventional activation gate at the intracellular end of the pore. The desensitized structure illustrates molecular determinants for alphaxalone binding to an inter-subunit TMD site. These structures suggest a plausible signaling pathway from alphaxalone binding at the bottom of the TMD to the channel gate in the pore-lining TM2 through the TM1–TM2 linker. The study provides a framework to discover new GABAAR modulators with therapeutic potential.