Unequal Chances: Family Background and Economic Success

Is the United States "the land of equal opportunity" or is the playing field tilted in favor of those whose parents are wealthy, well educated, and white? If family background is important in getting ahead, why? And if the processes that transmit economic status from parent to child are unfair, could public policy address the problem? Unequal Chances provides new answers to these questions by leading economists, sociologists, biologists, behavioral geneticists, and philosophers. New estimates show that intergenerational inequality in the United States is far greater than was previously thought. Moreover, while the inheritance of wealth and the better schooling typically enjoyed by the children of the well-to-do contribute to this process, these two standard explanations fail to explain the extent of intergenerational status transmission. The genetic inheritance of IQ is even less important. Instead, parent-offspring similarities in personality and behavior may play an important role. Race contributes to the process, and the intergenerational mobility patterns of African Americans and European Americans differ substantially. Following the editors' introduction are chapters by Greg Duncan, Ariel Kalil, Susan E. Mayer, Robin Tepper, and Monique R. Payne; Bhashkar Mazumder; David J. Harding, Christopher Jencks, Leonard M. Lopoo, and Susan E. Mayer; Anders Björklund, Markus Jäntti, and Gary Solon; Tom Hertz; John C. Loehlin; Melissa Osborne Groves; Marcus W. Feldman, Shuzhuo Li, Nan Li, Shripad Tuljapurkar, and Xiaoyi Jin; and Adam Swift.family background, economic success, education, status, public policy, inequality, genetic inheritance, intergenerational mobility

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels

Gaussian processes (GPs) produce good probabilistic models of functions, but most GP kernels require $O((n+m)n^2)$ time, where $n$ is the number of data points and $m$ the number of predictive locations. We present a new kernel that allows for Gaussian process regression in $O((n+m)\log(n+m))$ time. Our "binary tree" kernel places all data points on the leaves of a binary tree, with the kernel depending only on the depth of the deepest common ancestor. We can store the resulting kernel matrix in $O(n)$ space in $O(n \log n)$ time, as a sum of sparse rank-one matrices, and approximately invert the kernel matrix in $O(n)$ time. Sparse GP methods also offer linear run time, but they predict less well than higher dimensional kernels. On a classic suite of regression tasks, we compare our kernel against Mat\'ern, sparse, and sparse variational kernels. The binary tree GP assigns the highest likelihood to the test data on a plurality of datasets, usually achieves lower mean squared error than the sparse methods, and often ties or beats the Mat\'ern GP. On large datasets, the binary tree GP is fastest, and much faster than a Mat\'ern GP.Comment: NeurIPS 2022; 9 pages + appendice

Ecophysiological traits of grasses: resolving the effects of photosynthetic pathway and phylogeny

C4 photosynthesis is an important example of convergent evolution in plants, having arisen in eudicots, monocots and diatoms. Comparisons between such diverse groups are confounded by phylogenetic and ecological differences, so that only broad generalisations can be made about the role of C4 photosynthesis in
determining ecophysiological traits. However, 60% of C4 species occur in the grasses (Poaceae) and molecular phylogenetic techniques confirm that there are between 8 and 17 independent origins of C4 photosynthesis in the Poaceae. In a screening experiment, we compared leaf physiology and growth traits across several major
independent C3 & C4 groups within the Poaceae, asking 1) which traits differ consistently between photosynthetic
types and 2) which traits differ consistently between clades within each photosynthetic type

Futures in primary science education – connecting students to place and ecojustice

After providing a background to futures thinking in science, and exploring the literature around transdisciplinary approaches to curriculum, we present a futures pedagogy. We detail case studies from a year-long professional learning action research project during which primary school teachers developed curriculum for the Anthropocene, focusing on the topic of fresh water. Why fresh water? Living in South Australia—the driest state in the driest continent—water is a scarce and precious resource, and our main water supply, the River Murray, is in trouble. Water is an integral part of Earth’s ecosystem and plays a vital role in our survival (Flannery, 2010; Laszlo, 2014). Water literacy therefore has a genuine and important place in the school curriculum.Working with teachers and their students, the Water Literacies Project provided an ideal opportunity to explore a range of pedagogical approaches and practices which connect students to their everyday world, both now and in their possible futures, through place-based learning. We describe the use of futures scenario writing in an issues-based transdisciplinary curriculum unit on the theme of Water, driven by Year 5 teachers and their students from three primary schools: two located on the River Murray and one near metropolitan Adelaide. All three schools focused on a local wetland. The research was informed by teacher interviews, student and teacher journals, student work samples, and teacher presentations at workshops and conferences. We report on two aspects of the project: (1) the implementation of futures pedagogy, including the challenges it presented to the teachers and their students and (2) an emerging analysis of students’ views of the future and implications for further work around the futures pedagogical framework. Personal stories in relation to water, prior knowledge on the nature of water, experiential excursions to learn about water ecology and stories that examine the cultural significance of water—locally and not so locally—are featured (Lloyd, 2011; Paige & Lloyd, 2016). The outcome of our project is the development of comprehensive adventurous transdisciplinary units of work around water and connection to local place

Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation

In this paper, we aim at recovering an unknown signal x0 from noisy L1measurements y=Phi*x0+w, where Phi is an ill-conditioned or singular linear operator and w accounts for some noise. To regularize such an ill-posed inverse problem, we impose an analysis sparsity prior. More precisely, the recovery is cast as a convex optimization program where the objective is the sum of a quadratic data fidelity term and a regularization term formed of the L1-norm of the correlations between the sought after signal and atoms in a given (generally overcomplete) dictionary. The L1-sparsity analysis prior is weighted by a regularization parameter lambda>0. In this paper, we prove that any minimizers of this problem is a piecewise-affine function of the observations y and the regularization parameter lambda. As a byproduct, we exploit these properties to get an objectively guided choice of lambda. In particular, we develop an extension of the Generalized Stein Unbiased Risk Estimator (GSURE) and show that it is an unbiased and reliable estimator of an appropriately defined risk. The latter encompasses special cases such as the prediction risk, the projection risk and the estimation risk. We apply these risk estimators to the special case of L1-sparsity analysis regularization. We also discuss implementation issues and propose fast algorithms to solve the L1 analysis minimization problem and to compute the associated GSURE. We finally illustrate the applicability of our framework to parameter(s) selection on several imaging problems

Who funded the research behind the Oxford-AstraZeneca COVID-19 vaccine?

Objectives The Oxford-AstraZeneca COVID-19 vaccine (ChAdOx1 nCoV-19, Vaxzevira or Covishield) builds on two decades of research and development (R&D) into chimpanzee adenovirus-vectored vaccine (ChAdOx) technology at the University of Oxford. This study aimed to approximate the funding for the R&D of ChAdOx and the Oxford-AstraZeneca vaccine and to assess the transparency of funding reporting mechanisms. Methods We conducted a scoping review and publication history analysis of the principal investigators to reconstruct R&D funding the ChAdOx technology. We matched award numbers with publicly accessible grant databases. We filed freedom of information (FOI) requests to the University of Oxford for the disclosure of all grants for ChAdOx R&D. Results We identified 100 peer-reviewed articles relevant to ChAdOx technology published between January 2002 and October 2020, extracting 577 mentions of funding bodies from acknowledgements. Government funders from overseas (including the European Union) were mentioned 158 times (27.4%), the UK government 147 (25.5%) and charitable funders 138 (23.9%). Grant award numbers were identified for 215 (37.3%) mentions; amounts were publicly available for 121 (21.0%). Based on the FOIs, until December 2019, the biggest funders of ChAdOx R&D were the European Commission (34.0%), Wellcome Trust (20.4%) and Coalition for Epidemic Preparedness Innovations (17.5%). Since January 2020, the UK government contributed 95.5% of funding identified. The total identified R&D funding was £104 226 076 reported in the FOIs and £228 466 771 reconstructed from the literature search. Conclusion Our study approximates that public and charitable financing accounted for 97%-99% of identifiable funding for the ChAdOx vaccine technology research at the University of Oxford underlying the Oxford-AstraZeneca vaccine until autumn 2020. We encountered a lack of transparency in research funding reporting

Concurrent Imaging of Synaptic Vesicle Recycling and Calcium Dynamics

Synaptic transmission involves the calcium dependent release of neurotransmitter from synaptic vesicles. Genetically encoded optical probes emitting different wavelengths of fluorescent light in response to neuronal activity offer a powerful approach to understand the spatial and temporal relationship of calcium dynamics to the release of neurotransmitter in defined neuronal populations. To simultaneously image synaptic vesicle recycling and changes in cytosolic calcium, we developed a red-shifted reporter of vesicle recycling based on a vesicular glutamate transporter, VGLUT1-mOrange2 (VGLUT1-mOr2), and a presynaptically localized green calcium indicator, synaptophysin-GCaMP3 (SyGCaMP3) with a large dynamic range. The fluorescence of VGLUT1-mOr2 is quenched by the low pH of synaptic vesicles. Exocytosis upon electrical stimulation exposes the luminal mOr2 to the neutral extracellular pH and relieves fluorescence quenching. Reacidification of the vesicle upon endocytosis again reduces fluorescence intensity. Changes in fluorescence intensity thus monitor synaptic vesicle exo- and endocytosis, as demonstrated previously for the green VGLUT1-pHluorin. To monitor changes in calcium, we fused the synaptic vesicle protein synaptophysin to the recently improved calcium indicator GCaMP3. SyGCaMP3 is targeted to presynaptic varicosities, and exhibits changes in fluorescence in response to electrical stimulation consistent with changes in calcium concentration. Using real time imaging of both reporters expressed in the same synapses, we determine the time course of changes in VGLUT1 recycling in relation to changes in presynaptic calcium concentration. Inhibition of P/Q- and N-type calcium channels reduces calcium levels, as well as the rate of synaptic vesicle exocytosis and the fraction of vesicles released