Survivor-complier effects in the presence of selection on treatment, with application to a study of prompt ICU admission

Pre-treatment selection or censoring (`selection on treatment') can occur when two treatment levels are compared ignoring the third option of neither treatment, in `censoring by death' settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument

Parallel density matrix propagation in spin dynamics simulations

Several methods for density matrix propagation in distributed computing environments, such as clusters and graphics processing units, are proposed and evaluated. It is demonstrated that the large communication overhead associated with each propagation step (two-sided multiplication of the density matrix by an exponential propagator and its conjugate) may be avoided and the simulation recast in a form that requires virtually no inter-thread communication. Good scaling is demonstrated on a 128-core (16 nodes, 8 cores each) cluster.Comment: Submitted for publicatio

The physiology of protein S-acylation

Protein S-acylation, the only fully reversible posttranslational lipid modification of proteins, is emerging as a ubiquitous mechanism to control the properties and function of a diverse array of proteins and consequently physiological processes. S-acylation results from the enzymatic addition of long-chain lipids, most typically palmitate, onto intracellular cysteine residues of soluble and transmembrane proteins via a labile thioester linkage. Addition of lipid results in increases in protein hydrophobicity that can impact on protein structure, assembly, maturation, trafficking, and function. The recent explosion in global S-acylation (palmitoyl) proteomic profiling as a result of improved biochemical tools to assay S-acylation, in conjunction with the recent identification of enzymes that control protein S-acylation and de-acylation, has opened a new vista into the physiological function of S-acylation. This review introduces key features of S-acylation and tools to interrogate this process, and highlights the eclectic array of proteins regulated including membrane receptors, ion channels and transporters, enzymes and kinases, signaling adapters and chaperones, cell adhesion, and structural proteins. We highlight recent findings correlating disruption of S-acylation to pathophysiology and disease and discuss some of the major challenges and opportunities in this rapidly expanding field

Magnetic Ground State of Pr0.89_{0.89}LaCe0.11_{0.11}CuO4+α−δ_{4+\alpha-\delta} with Varied Oxygen Depletion Probed by Muon Spin Relaxation

The magnetic ground state of an electron-doped cuprate superconductor Pr$_{1-x}$LaCe$_x$CuO$_{4+\alpha-\delta}$ ($x=0.11, \alpha\simeq0.04$) has been studied by means of muon spin rotation/relaxation (\msr) over a wide variety of oxygen depletion, $0.03\le\delta\le0.12$. Appearance of weak random magnetism over entire crystal volume has been revealed by a slow exponential relaxation. The absence of $\delta$-dependence for the random magnetism and the multiplet pattern of muon Knight shift at higher fields strongly suggest that the random moments are associated with excited Pr$^{3+}$ ions under crystal electric field.Comment: 6 pages, 4 figures, submitted to J. Phys. Soc. Jp

The importance of including habitat-specific behaviour in models of butterfly movement

Dispersal is a key process affecting population persistence and major factors affecting dispersal rates are the amounts, connectedness and properties of habitats in landscapes. We present new data on the butterfly Maniola jurtina in flower-rich and flower-poor habitats that demonstrates how movement and behaviour differ between sexes and habitat types, and how this effects consequent dispersal rates. Females had higher flight speeds than males but their total time in flight was four times less. The effect of habitat type was strong for both sexes, flight speeds were ~2.5x and ~1.7x faster on resource-poor habitats for males and females respectively, and flights were approximately 50% longer. With few exceptions females oviposited in the mown grass habitat, likely because growing grass offers better food for emerging caterpillars, but they foraged in the resource-rich habitat. It seems that females faced a trade-off between ovipositing without foraging in the mown grass or foraging without ovipositing where flowers were abundant. We show that taking account of habitat-dependent differences in activity, here categorised as flight or non-flight, is crucial to obtaining good fits of an individual-based model to observed movement. An important implication of this finding is that incorporating habitat-specific activity budgets is likely necessary for predicting longer-term dispersal in heterogeneous habitats as habitat-specific behaviour substantially influences the mean (>30% difference) and kurtosis (1.4x difference) of dispersal kernels. The presented IBMs provide a simple method to explicitly incorporate known activity and movement rates when predicting dispersal in changing and heterogeneous landscapes

Machine learning -- based diffractive imaging with subwavelength resolution

Far-field characterization of small objects is severely constrained by the diffraction limit. Existing tools achieving sub-diffraction resolution often utilize point-by-point image reconstruction via scanning or labelling. Here, we present a new imaging technique capable of fast and accurate characterization of two-dimensional structures with at least wavelength/25 resolution, based on a single far-field intensity measurement. Experimentally, we realized this technique resolving the smallest-available to us 180-nm-scale features with 532-nm laser light. A comprehensive analysis of machine learning algorithms was performed to gain insight into the learning process and to understand the flow of subwavelength information through the system. Image parameterization, suitable for diffractive configurations and highly tolerant to random noise was developed. The proposed technique can be applied to new characterization tools with high spatial resolution, fast data acquisition, and artificial intelligence, such as high-speed nanoscale metrology and quality control, and can be further developed to high-resolution spectroscop

Comprehension as social and intellectual practice: Rebuilding curriculum in low socioeconomic and cultural minority schools

This article reframes the concept of comprehension as a social and intellectual practice. It reviews current approaches to reading instruction for linguistically and culturally diverse and low socioeconomic students, noting an emphasis on comprehension as autonomous skills. The Four Resources model (Freebody & Luke, 1990) is used to make the case for the integration of comprehension instruction with an emphasis on student cultural and community knowledge, and substantive intellectual and sociocultural content in elementary school curricula. Illustrations are drawn from research underway on the teaching of literacy in primary schools in low SES communities