Maternal Migration Background and Mortality Among Infants Born Extremely Preterm

IMPORTANCE: Extremely preterm infants require care provided in neonatal intensive care units (NICUs) to survive. In the Netherlands, a decision is made regarding active treatment between 24 weeks 0 days and 25 weeks 6 days after consultation with the parents.OBJECTIVE: To investigate the association between maternal migration background and admissions to NICUs and mortality within the first year among extremely preterm infants.DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study linked data of registered births in the Netherlands with household-level income tax records and municipality and mortality registers. Eligible participants were households with live births at 24 weeks 0 days to 25 weeks 6 days gestation between January 1, 2010, and December 31, 2017. Data linkage and analysis was performed from March 1, 2020, to June 30, 2023.EXPOSURE: Maternal migration background, defined as no migration background vs first- or second-generation migrant mother.MAIN OUTCOMES AND MEASURES: Admissions to NICUs and mortality within the first week, month, and year of life. Logistic regressions were estimated adjusted for year of birth, maternal age, parity, household income, sex, gestational age, multiple births, and small for gestational age. NICU-specific fixed effects were also included.RESULTS: Among 1405 live births (768 male [54.7%], 546 [38.9%] with maternal migration background), 1243 (88.5%) were admitted to the NICU; 490 of 546 infants (89.7%) born to mothers with a migration background vs 753 of 859 infants (87.7%) born to mothers with no migration background were admitted to NICU (fully adjusted RR, 1.03; 95% CI, 0.99-1.08). A total of 652 live-born infants (46.4%) died within the first year of life. In the fully adjusted model, infants born to mothers with a migration background had lower risk of mortality within the first week (RR, 0.81; 95% CI, 0.66-0.99), month (RR, 0.84; 95% CI, 0.72-0.97), and year of life (RR, 0.85; 95% CI, 0.75-0.96) compared with infants born to mothers with no migration background.CONCLUSIONS:In this nationally representative cross-sectional study, infants born to mothers with a migration background at 24 weeks 0 days to 25 weeks 6 days of gestation in the Netherlands had lower risk of mortality within the first year of life than those born to mothers with no migration background, a result that was unlikely to be explained by mothers from different migration backgrounds attending different NICUs or differential preferences for active obstetric management across migration backgrounds. Further research is needed to understand the underlying mechanisms driving these disparities, including parental preferences for active care of extremely preterm infants.</p

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.Comment: The 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018

Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making

Machine learning (ML) is increasingly being used in image retrieval systems for medical decision making. One application of ML is to retrieve visually similar medical images from past patients (e.g. tissue from biopsies) to reference when making a medical decision with a new patient. However, no algorithm can perfectly capture an expert's ideal notion of similarity for every case: an image that is algorithmically determined to be similar may not be medically relevant to a doctor's specific diagnostic needs. In this paper, we identified the needs of pathologists when searching for similar images retrieved using a deep learning algorithm, and developed tools that empower users to cope with the search algorithm on-the-fly, communicating what types of similarity are most important at different moments in time. In two evaluations with pathologists, we found that these refinement tools increased the diagnostic utility of images found and increased user trust in the algorithm. The tools were preferred over a traditional interface, without a loss in diagnostic accuracy. We also observed that users adopted new strategies when using refinement tools, re-purposing them to test and understand the underlying algorithm and to disambiguate ML errors from their own errors. Taken together, these findings inform future human-ML collaborative systems for expert decision-making

Factors affecting pre-failure instability of sand under plane-strain conditions

Experimental data obtained from a plane-strain appara- tus are presented in this paper to show that a pre-failure instability in the form of a rapid and sustained increase in strain rate can occur for both contractive and dilative sand under fully drained conditions. However, this type of instability is different from the runaway type of instability observed under undrained conditions, and has therefore been called conditional instability. Despite the differences, the conditions for both types of instability are the same for contractive sand. There are also other factors that affect the pre-failure instability of sand ob- served in the laboratory. These include the stress ratio, void ratio, sand state, load control mode and reduction rate of the effective confining stress. In this paper, these factors are discussed and analysed using experimental data obtained from undrained instability (or creep) tests and constant shear drained (CSD) tests carried out under plane-strain conditions

Supersymmetry Without Prejudice

We begin an exploration of the physics associated with the general CP-conserving MSSM with Minimal Flavor Violation, the pMSSM. The 19 soft SUSY breaking parameters in this scenario are chosen so as to satisfy all existing experimental and theoretical constraints assuming that the WIMP is a conventional thermal relic, ie, the lightest neutralino. We scan this parameter space twice using both flat and log priors for the soft SUSY breaking mass parameters and compare the results which yield similar conclusions. Detailed constraints from both LEP and the Tevatron searches play a particularly important role in obtaining our final model samples. We find that the pMSSM leads to a much broader set of predictions for the properties of the SUSY partners as well as for a number of experimental observables than those found in any of the conventional SUSY breaking scenarios such as mSUGRA. This set of models can easily lead to atypical expectations for SUSY signals at the LHC.Comment: 61 pages, 24 figs. Refs., figs, and text added, typos fixed; This version has reduced/bitmapped figs. For a version with better figs please go to http://www.slac.stanford.edu/~rizz