Finding one's place: shifting ethnic identities of recent immigrant children from China, Haiti and Mexico in the United States

This article examines the ethnic identity adaptations of recently-arrived immigrant children from China, Haiti, and Mexico. Overall, three main types of ethnic identity categories emerged: country of origin (e.g., Chinese), hyphenated (e.g., Chinese American), and pan-ethnic (e.g., Asian or Asian American). These three ethnic identities were examined to assess their relationships with various social and structural variables. While most of the participants retained their country-of-origin label throughout the five-year longitudinal study, a significant number of them showed divergent paths of ethnic identity shifts and formations. As a whole, only gender, annual household income, and parental educational level were significantly associated with different ethnic identity changes. Analyzed separately by national groups, Chinese participants’ ethnic identity adaptations were influenced by parental educational level, and Haitian and Mexican participants by gender. Potential explanations for the various ethnic adaptations are examined and limitations of the study discussed

Public Works and Employment Programmes: Towards a Long-Term Development Approach

Public works and employment programmes have long been considered a staple of social assistance. For the most part, though, they have been designed as short-term ?safety nets?. While, in some cases, the focus has also been on reducing poverty or addressing structural unemployment challenges, their implementation has seldom been on a scale that would make a dent in structural poverty. The fact that large scale programmes such as India?s National Rural Employment Guarantee Scheme (NREGS), which was initiated prior to the recent economic crisis, could also be effective in responding speedily to and mitigating the effects of the crisis has elicited interest in such policies as a component of inclusive growth paths. In making the case for a longer-term development approach, the paper points out that such an approach would not only allow these programmes to act as shock absorbers without being ?too little, too late? but would also enable the state to strengthen its capacities to provide support to livelihood strategies of the poor through addressing critical public goods and service deficits while creating jobs. In this context, the paper assesses the desirability and feasibility of adopting a universal or a partial Employment Guarantee (EG) to make such programmes a more stable complement to market-driven employment creation in situations where levels of poverty, in particular, working poverty, and underemployment are high. The paper also explores the complementarities and interactions with various social assistance and cash transfer programmes with a view to fostering a more comprehensive approach to social protection for the poor. The paper concludes with a section on implementation issues with a view to strengthening learning on how to plan, design and implement long-term and Employment Guarantee types of public employment programmes. (?)Public Works and Employment Programmes: Towards a Long-Term Development Approach

Knowledge-informed Data-driven Modeling of Microbial Inactivation in Food

Prevention of the growth of harmful microorganisms in food products is an important requirement for ensuring food safety and quality. Mathematical models to predict the quantitative changes in microbial populations in food to the variations of environmental conditions are useful tools in this regard. Current approaches that use empirical formulation generate arbitrary forms of model equations, impeding systematic analysis towards identifying key factors governing microbial growth and inactivation in food. To address this challenge, we present a data-driven modeling pipeline that enables automatic discovery of model equations (through parsimonious selection of relevant terms in a pre-built library) without having to assume specific functional forms of equations a priori. Through case studies using literature data, we showed how one can systematically build and analyze microbial inactivation models using the pipeline to predict the changes in D-value (i.e., the time taken to reduce microbial population to 10% of the initial level) as a function of given input variables. We used Akaike information criterion to avoid overfitting without hurting model accuracy. The final model was integrated with global sensitivity analysis to evaluate the impacts of individual factors on target variables. We highlight that, besides enhanced performance in data fit, the ability to generate models of varying complexity by accounting for a trade-off between accuracy and interpretability is a unique feature with our approach, not shared by empirical methods. Due to its generality, the pipeline presented in this work is readily applicable to many other related non-linear systems without being limited to microbial inactivation dataset

Associations of Muscle Mass and Strength with All-Cause Mortality among US Older Adults

INTRODUCTION: Recent studies suggested that muscle mass and muscle strength may independently or synergistically affect aging-related health outcomes in older adults; however, prospective data on mortality in the general population are sparse. METHODS: We aimed to prospectively examine individual and joint associations of low muscle mass and low muscle strength with all-cause mortality in a nationally representative sample. This study included 4449 participants age 50 yr and older from the National Health and Nutrition Examination Survey 1999 to 2002 with public use 2011 linked mortality files. Weighted multivariable logistic regression models were adjusted for age, sex, race, body mass index (BMI), smoking, alcohol use, education, leisure time physical activity, sedentary time, and comorbid diseases. RESULTS: Overall, the prevalence of low muscle mass was 23.1% defined by appendicular lean mass (ALM) and 17.0% defined by ALM/BMI, and the prevalence of low muscle strength was 19.4%. In the joint analyses, all-cause mortality was significantly higher among individuals with low muscle strength, whether they had low muscle mass (odds ratio [OR], 2.03; 95% confidence interval [CI], 1.27-3.24 for ALM; OR, 2.53; 95% CI, 1.64-3.88 for ALM/BMI) or not (OR, 2.66; 95% CI, 1.53-4.62 for ALM; OR, 2.17; 95% CI, 1.29-3.64 for ALM/BMI). In addition, the significant associations between low muscle strength and all-cause mortality persisted across different levels of metabolic syndrome, sedentary time, and LTPA. CONCLUSIONS: Low muscle strength was independently associated with elevated risk of all-cause mortality, regardless of muscle mass, metabolic syndrome, sedentary time, or LTPA among US older adults, indicating the importance of muscle strength in predicting aging-related health outcomes in older adults

An Automated Treatment Plan Quality Control Tool for Intensity-Modulated Radiation Therapy Using a Voxel-Weighting Factor-Based Re-Optimization Algorithm.

Intensity-modulated radiation therapy (IMRT) currently plays an important role in radiotherapy, but its treatment plan quality can vary significantly among institutions and planners. Treatment plan quality control (QC) is a necessary component for individual clinics to ensure that patients receive treatments with high therapeutic gain ratios. The voxel-weighting factor-based plan re-optimization mechanism has been proved able to explore a larger Pareto surface (solution domain) and therefore increase the possibility of finding an optimal treatment plan. In this study, we incorporated additional modules into an in-house developed voxel weighting factor-based re-optimization algorithm, which was enhanced as a highly automated and accurate IMRT plan QC tool (TPS-QC tool). After importing an under-assessment plan, the TPS-QC tool was able to generate a QC report within 2 minutes. This QC report contains the plan quality determination as well as information supporting the determination. Finally, the IMRT plan quality can be controlled by approving quality-passed plans and replacing quality-failed plans using the TPS-QC tool. The feasibility and accuracy of the proposed TPS-QC tool were evaluated using 25 clinically approved cervical cancer patient IMRT plans and 5 manually created poor-quality IMRT plans. The results showed high consistency between the QC report quality determinations and the actual plan quality. In the 25 clinically approved cases that the TPS-QC tool identified as passed, a greater difference could be observed for dosimetric endpoints for organs at risk (OAR) than for planning target volume (PTV), implying that better dose sparing could be achieved in OAR than in PTV. In addition, the dose-volume histogram (DVH) curves of the TPS-QC tool re-optimized plans satisfied the dosimetric criteria more frequently than did the under-assessment plans. In addition, the criteria for unsatisfied dosimetric endpoints in the 5 poor-quality plans could typically be satisfied when the TPS-QC tool generated re-optimized plans without sacrificing other dosimetric endpoints. In addition to its feasibility and accuracy, the proposed TPS-QC tool is also user-friendly and easy to operate, both of which are necessary characteristics for clinical use

Tempered Sigmoid Activations for Deep Learning with Differential Privacy

Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data. In practice, this has been mostly an afterthought, with privacy-preserving models obtained by re-running training with a different optimizer, but using the model architectures that already performed well in a non-privacy-preserving setting. This approach leads to less than ideal privacy/utility tradeoffs, as we show here. Instead, we propose that model architectures are chosen ab initio explicitly for privacy-preserving training. To provide guarantees under the gold standard of differential privacy, one must bound as strictly as possible how individual training points can possibly affect model updates. In this paper, we are the first to observe that the choice of activation function is central to bounding the sensitivity of privacy-preserving deep learning. We demonstrate analytically and experimentally how a general family of bounded activation functions, the tempered sigmoids, consistently outperform unbounded activation functions like ReLU. Using this paradigm, we achieve new state-of-the-art accuracy on MNIST, FashionMNIST, and CIFAR10 without any modification of the learning procedure fundamentals or differential privacy analysis