scholarly journals Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 780
Author(s):  
Shuyu Tian ◽  
Rory Stevens ◽  
Bridget T. McInnes ◽  
Nastassja A. Lewinski
Keyword(s):  
Cell Viability ◽  
Regression Models ◽  
Input Parameter ◽  
Training Data ◽  
Extrusion Pressure ◽  
Filament Diameter ◽  
Literature Source ◽  
Experimental Parameters ◽  
Using Data ◽  
Experimental Values

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time- and resource-intensive and not easily translatable to other laboratories. This study approaches EBB parameter optimization through machine learning (ML) models trained using data collected from the published literature. We investigated regression-based and classification-based ML models and their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite bioinks. In addition, we interrogated if regression-based models can predict suitable extrusion pressure given the desired cell viability when keeping other experimental parameters constant. We also compared models trained across data from general literature to models trained across data from one literature source that utilized alginate and gelatin bioinks. The results indicate that models trained on large amounts of data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predict cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of 70%, the cell viability predictions remained constant despite altering input parameter combinations. Our trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design.

Urban Homeownership and Mental Health: Mediating Effect of Perceived Sense of Control

2012 ◽  
Vol 11 (4) ◽  
pp. 409-430 ◽  
Author(s):  
Kim R. Manturuk
Keyword(s):  
Mental Health ◽  
Low Income ◽  
Urban Areas ◽  
Regression Models ◽  
Mediating Effect ◽  
Sense Of Control ◽  
Using Data ◽  
The Relationship ◽  
Subjective Trust ◽  
Control Part

What are the mechanisms responsible for homeowners’ better mental health? Social disorganization theory suggests that the relationship between homeownership and mental health is mediated by perceived sense of control, trust in neighbors, and residential stability. This hypothesis is tested using data collected from respondents in 30 low–wealth urban areas. Using propensity score matching and regression models, I find that low–income homeowners report a greater sense of control and trust in their neighbors than comparable renters. Homeownership likewise has an impact on mental health, but the effect is entirely mediated by perceived sense of control. Part of that mediating effect is related to avoiding serious delinquency in mortgage payments. However, subjective trust and residential mobility did not mediate the relationship between homeownership and mental health. The study findings are discussed in light of the need for a cohesive theory of homeownership, particularly given changing economic realities.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

scholarly journals Shadow Education in Germany: Compensatory or Status Attainment Strategy? Findings from the German LifE Study

2020 ◽  
Vol 7 (2-2019) ◽  
pp. 143-159 ◽  
Author(s):  
Steve R. Entrich ◽  
Wolfgang Lauterbach
Keyword(s):  
Performance Improvement ◽  
Social Reproduction ◽  
Regression Models ◽  
Status Attainment ◽  
Strong Increase ◽  
Shadow Education ◽  
Competitive Advantages ◽  
Life Study ◽  
German Life ◽  
Using Data

In Germany we observe a strong increase in the enrolment in shadow education (‘Nachhilfe’) over the last two decades. To explain this development we draw on social reproduction theories identifying two strategies: (1) families seek competitive advantages for their children to maintain or achieve an advantageous education level (status attainment strategy); and (2) families seek performance improvement for their low performing children in order to meet the high demands in the pursuit of the highest school diploma (compensatory strategy). To test our theoretical ideas, we estimate regression models using data from the 2012 German LifE study. We find that shadow education is primarily used by disadvantaged educational strata to deal with higher demands in school. We conclude that the increased investment in Nachhilfe is an unintended but not yet negative outcome of educational expansion and recent educational reforms in Germany.

scholarly journals Using machine learning to predict extreme events in complex systems

2019 ◽  
Vol 117 (1) ◽  
pp. 52-59 ◽  
Author(s):  
Di Qi ◽  
Andrew J. Majda
Keyword(s):  
Dynamical Systems ◽  
Learning Strategies ◽  
Extreme Events ◽  
Water Waves ◽  
Extreme Values ◽  
Training Data ◽  
Partition Functions ◽  
Grand Challenge ◽  
Natural Systems ◽  
Using Data

Extreme events and the related anomalous statistics are ubiquitously observed in many natural systems, and the development of efficient methods to understand and accurately predict such representative features remains a grand challenge. Here, we investigate the skill of deep learning strategies in the prediction of extreme events in complex turbulent dynamical systems. Deep neural networks have been successfully applied to many imaging processing problems involving big data, and have recently shown potential for the study of dynamical systems. We propose to use a densely connected mixed-scale network model to capture the extreme events appearing in a truncated Korteweg–de Vries (tKdV) statistical framework, which creates anomalous skewed distributions consistent with recent laboratory experiments for shallow water waves across an abrupt depth change, where a remarkable statistical phase transition is generated by varying the inverse temperature parameter in the corresponding Gibbs invariant measures. The neural network is trained using data without knowing the explicit model dynamics, and the training data are only drawn from the near-Gaussian regime of the tKdV model solutions without the occurrence of large extreme values. A relative entropy loss function, together with empirical partition functions, is proposed for measuring the accuracy of the network output where the dominant structures in the turbulent field are emphasized. The optimized network is shown to gain uniformly high skill in accurately predicting the solutions in a wide variety of statistical regimes, including highly skewed extreme events. The technique is promising to be further applied to other complicated high-dimensional systems.

scholarly journals Dietary phytoestrogen intakes of adult women are not strongly related to fecundability in 2 preconception cohort studies

2020 ◽  
Vol 150 (5) ◽  
pp. 1240-1251 ◽  
Author(s):  
Amelia K Wesselink ◽  
Elizabeth E Hatch ◽  
Ellen M Mikkelsen ◽  
Ellen Trolle ◽  
Sydney K Willis ◽  
...  
Keyword(s):  
Cohort Studies ◽  
Luteal Phase ◽  
Regression Models ◽  
Adult Women ◽  
Hormonal Activity ◽  
Web Based ◽  
Pregnancy Status ◽  
Using Data ◽  
Study Participants

ABSTRACT Background Phytoestrogens are plant-derived hormonally active compounds found in soy, cruciferous vegetables, nuts, and seeds. Although phytoestrogens have been associated with altered endogenous hormonal activity, luteal phase deficiency, and reduced endometrial decidualization, the literature reporting examinations of phytoestrogen intake and fertility presents mixed findings. Objectives We sought to evaluate prospectively the association between dietary phytoestrogen intake (isoflavones, lignans, and coumestans) and fecundability, the per-cycle probability of conception, in 2 cohorts of women planning pregnancy. Methods Pregnancy Study Online (PRESTO) and Snart Foraeldre (SF) are parallel web-based preconception cohort studies of women from North America and Denmark, respectively, who are trying to conceive. Participants complete an online baseline questionnaire on sociodemographic, lifestyle, and medical factors. We ascertained intake of individual phytoestrogens from validated FFQs. We measured fecundability using data on menstruation and pregnancy status from bimonthly follow-up questionnaires. We analyzed data from 4880 PRESTO and 2898 SF female study participants who had been attempting conception for ≤6 cycles at study entry. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% CIs. Results Phytoestrogen intake varied across cohorts, yet was associated with higher socioeconomic status and healthier behaviors in both cohorts. After adjustment for potential confounders, phytoestrogen intake was not substantially associated with fecundability in either cohort. We observed some evidence of improved fecundability with increasing isoflavone intake among women age ≥30 years in PRESTO (FR: 1.12; 95% CI: 0.94, 1.34, for comparison of ≥90th with <25th percentile intake) and SF (corresponding FR: 1.19; 95% CI: 0.92, 1.55). Lignan intake was associated with slightly increased fecundability in SF (FR for comparison of 75th to 90th with <25th percentile: 1.10; 95% CI: 0.96, 1.26), but decreased fecundability in PRESTO (FR for comparison of ≥90th with <25th percentile: 0.83; 95% CI: 0.72, 0.97). Conclusions We did not observe strong associations between phytoestrogen intake and prospectively-measured fecundability among North American or Danish pregnancy planners.

scholarly journals Parenting Stress and the Use of Formal and Informal Child Care: Associations for Fathers and Mothers

2018 ◽  
Vol 39 (12) ◽  
pp. 3203-3224 ◽  
Author(s):  
Lyn Craig ◽  
Brendan Churchill
Keyword(s):  
Parenting Stress ◽  
Household Income ◽  
Regression Models ◽  
Family Care ◽  
Formal Care ◽  
Panel Survey ◽  
Family Carers ◽  
Stress Scale ◽  
Using Data ◽  
Employed Parents

We investigated relationships between nonparental care and psychological strains of parenthood. Using data from employed parents of children below 5 years of age ( n = 6,886 fathers and mothers) from Waves 4 to 11 of the household panel survey Household, Income and Labour Dynamics in Australia (HILDA), we constructed a parenting stress scale from the average of four items (α = .76) administered in the Self-Completion Questionnaire. We ran panel random-effects regression models testing associations between amount and type of nonparental care and parenting stress, for both mothers and fathers. We distinguished between formal care, informal and family care (mainly grandparents), and mixed care. Results showed that fathers and mothers’ parenting stress is positively associated with hours of nonparental care, but that for both genders parenting stress is significantly lower if the care is provided by informal/family carers.

Propensity scores: From naïve enthusiasm to intuitive understanding

2011 ◽  
Vol 21 (3) ◽  
pp. 273-293 ◽  
Author(s):  
Elizabeth Williamson ◽  
Ruth Morley ◽  
Alan Lucas ◽  
James Carpenter
Keyword(s):  
Propensity Score ◽  
Regression Models ◽  
Propensity Scores ◽  
Causal Effect ◽  
Future Research ◽  
Propensity Score Methods ◽  
Advantages And Disadvantages ◽  
Alternative Approach ◽  
Using Data ◽  
Unmeasured Confounders

Estimation of the effect of a binary exposure on an outcome in the presence of confounding is often carried out via outcome regression modelling. An alternative approach is to use propensity score methodology. The propensity score is the conditional probability of receiving the exposure given the observed covariates and can be used, under the assumption of no unmeasured confounders, to estimate the causal effect of the exposure. In this article, we provide a non-technical and intuitive discussion of propensity score methodology, motivating the use of the propensity score approach by analogy with randomised studies, and describe the four main ways in which this methodology can be implemented. We carefully describe the population parameters being estimated — an issue that is frequently overlooked in the medical literature. We illustrate these four methods using data from a study investigating the association between maternal choice to provide breast milk and the infant's subsequent neurodevelopment. We outline useful extensions of propensity score methodology and discuss directions for future research. Propensity score methods remain controversial and there is no consensus as to when, if ever, they should be used in place of traditional outcome regression models. We therefore end with a discussion of the relative advantages and disadvantages of each.

Mechanical System Modelling Using Physics-Based and Data-Based Subsystems

2020 ◽  
Author(s):  
Siamak Arbatani ◽  
József Kövecses
Keyword(s):  
Mechanical System ◽  
Interaction Force ◽  
Point Of View ◽  
Training Data ◽  
System Modelling ◽  
Core Element ◽  
Interaction Forces ◽  
Force Systems ◽  
Using Data ◽  
Novel Concept

Abstract Mechanical systems have been traditionally represented using parametric physics-based models. In this work, we introduce a novel concept, in this part of the mechanical system is represented using data-based subsystem models, and the overall mechanical system model is composed of these data-based and other, physics-based subsystems. A core element is the interfacing of the subsystems, which gives rise to interaction forces. The interfacing problem is formulated in a way that makes it possible to give a general representation to the interaction forces. We demonstrate that from the point of view of the physics-based subsystems the important element is that the data-based models can represent the interaction force systems properly. The data-based subsystems are developed using deep recurrent neural networks, and the training data is generated based on simulations using the fully parametric physics-based model of the system. Such training data could also be obtained through physical experimentation.

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