Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustained maternal inflammation during the early third trimester yields intrauterine growth restriction, impaired skeletal muscle glucose metabolism, and diminished β cell function in fetal sheep.

Maternal inflammation causes fetal intrauterine growth restriction (IUGR), but its impact on fetal metabolism is not known. Thus, our objective was to determine the impact of sustained maternal inflammation in late gestation on fetal inflammation, skeletal muscle glucose metabolism, and insulin secretion. Pregnant ewes were injected every 3rd d from the 100th to 112th d of gestation (term = 150 d) with saline (controls) or lipopolysaccharide (LPS) to induce maternal inflammation and IUGR (MI-IUGR). Fetal femoral blood vessels were catheterized on d 118 in order to assess β cell function on d 123, hindlimb glucose metabolic rates on d 124, and daily blood parameters from d 120 to 125. Fetal muscle was isolated on d 125 to assess ex vivo glucose metabolism. Injection of LPS increased (P < 0.05) rectal temperatures, circulating white blood cells, and plasma TNFα concentrations in MI-IUGR ewes. Maternal leukocytes remained elevated (P < 0.05) and TNFα tended to remain elevated (P < 0.10) compared to controls almost 2 weeks after the final LPS injection. Total white blood cells, monocytes, granulocytes, and TNFα were also greater (P < 0.05) in MI-IUGR fetuses than controls over this period. MI-IUGR fetuses had reduced (P < 0.05) blood O2 partial pressures and greater (P < 0.05) maternofetal O2 gradients, but blood glucose and maternofetal glucose gradients did not differ from controls. Basal and glucose-stimulated insulin secretion were reduced (P < 0.05) by 32% and 42%, respectively, in MI-IUGR fetuses. In vivo hindlimb glucose oxidation did not differ between groups under resting conditions but was 47% less (P < 0.05) in MI-IUGR fetuses than controls during hyperinsulinemia. Hindlimb glucose utilization did not differ between fetal groups. At d 125, MI-IUGR fetuses were 22% lighter (P < 0.05) than controls and tended to have greater (P < 0.10) brain/bodyweight ratios. Ex vivo skeletal muscle glucose oxidation did not differ between groups in basal media but was less (P < 0.05) for MI-IUGR fetuses in insulin-spiked media. Glucose uptake rates and phosphorylated-to-total Akt ratios were less (P < 0.05) in muscle from MI-IUGR fetuses than controls regardless of media. We conclude that maternal inflammation leads to fetal inflammation, reduced β cell function, and impaired skeletal muscle glucose metabolism that persists after maternal inflammation ceases. Moreover, fetal inflammation may represent a target for improving metabolic dysfunction in IUGR fetuses.
Standardization of NPS measurement: interim report of AAPM TG16

This article reviews the state of the Noise Power Standard being drafted by Task Group No. 16 for the American Association of Physicists in Medicine. The Standard is intended to represent a consensus on acceptable practices in the measurement and reporting of noise power spectra for digital radiographic imaging devices based on single projections and to contain informative sections which will be of use to those not completely familiar with the measurement and interpretation of noise power spectra. Several of the issues considered by the committee are reviewed, including issues of conditioning and windowing data, issues specific to several modalities, and various methods of data presentation. A note on the historical background of noise power measurements and a brief discussion of possible avenues for future research is included.
Family care of older people in Europe

Developing appropriate responses to an ageing population is recognized by policy makers throughout the developed world as a top priority, as is the vital contribution made by family caregivers. However, cultural, demographic and organizational differences between countries have encouraged diverse patterns of response to this common challenge. This book provides a systematic cross-cultural analysis of contemporary patterns and future trends in all major countries of the European Union. Additional interest is provided by including Poland emerging from the post- Communist block as the country at the forefront for joining the European Union. The book should be useful to European policy makers and academies involved in studying the health and social care needs of older people and the capacity, contribution and needs of family caregivers who provide care to older people. The book is also relevant for policy makers and researchers in other countries, mostly in North America and Australia who wish to study European approaches to supporting older people and family caregivers. The practical focus of the book, which includes the use of case scenarios, means it should appeal to health and social care practitioners involved in day to day care of older people and their family caregivers. The book is not designed as a guide for individual family caregivers, more useful to those embarking on research in this area or entering the policy making field. It takes the reader to a high level of understanding and should be useful reading even for the most experienced policy maker and researcher as it provide updated insights resulting from its cross cultural approach.
Characterisation of microparticles and nitro-oxidative stress in cardiometabolic disease

Polycystic ovary syndrome (PCOS) is a common condition characterised by hyperandrogenism, oligo/anovulation and defects in insulin secretion and sensitivity. PCOS patients also have an increased prevalence of hypertension, dyslipidaemia and endothelial dysfunction, a state associated with decreased nitric oxide bioavailability and increased oxidative stress. Using women with PCOS as a model of predisposition to cardiovascular disease (CVD), the aim of this thesis was to provide a clearer understanding of mechanisms that may predispose individuals to endothelial dysfunction, and ultimately CVD. PCOS patients were compared to healthy controls in an observational study, which involved a comprehensive assessment of biochemical nitro-oxidative stress indices and a detailed characterisation of circulating microparticles (MPs). There was little evidence to suggest that women with PCOS have an increased oxidative stress compared to age/BMI-matched controls. However, PCOS patients did display elevated levels of annexin V positive MPs that were predominantly derived from platelets. In vitro studies investigated the effect of several metabolic stressors akin to those found in PCOS on endothelial-derived MP characteristics and function. Human endothelial (HECV) cells were exposed to oxidative, hypoxic, hyperandrogenic and metabolic stressors. Each metabolic stressor affected MP generation uniquely, suggesting MP characteristics and function reflect parental cell conditions. In order to determine whether circulating MP levels could be modulated in a clinical cohort, the effect of apheresis on circulating MP levels was investigated in patients with established CVD (familial hypercholesterolaemia). Apheresis decreased circulating levels of MPs and was associated with a decreased thrombin generation capacity in these patients. The data in this thesis thus provide evidence that young women with PCOS have an elevated concentration of annexin V positive MPs, even though there is little biochemical evidence for nitro-oxidative stress. Further studies are needed to assess the effect of this increase in circulating MPs on cardiovascular clinical end-points. In vitro experiments showed that the cellular stress condition is reflected in the MP characteristics, whereby each pathological stressor resulted in a unique MP phenotype. Furthermore, in patients with established CVD, apheresis reduced circulating levels of MPs. In conclusion, an elevated annexin V positive MP population may represent a novel mechanism by which cardiovascular risk is increased in patients with PCOS. These findings could have future implications for use as biomarkers, in diagnosis and therapeutics.
Modeling the Public Health Response to Bioterrorism

Over the past three years, the United States has been faced with three separate epidemic outbreaks that have demonstrated the importance of a system-wide preparedness for public health emergencies. The 2001 anthrax attacks, the 2002–2003 SARS (severe acute respiratory syndrome) epidemic, and the 2003 monkey pox outbreaks have all demonstrated the nation's vulnerability to infectious diseases of both natural and deliberate origins. The limited capacity for hospital-based treatment of patients in response to these outbreaks has been highlighted. Preparedness for epidemics has been studied only minimally in any standardized way. This article, therefore, reviews alternative approaches to the modeling and assessment of health system preparedness and response in order to provide a foundation for future standardized study. Keywords: bioterrorism; Centers for Disease Control and Prevention; public health system; response models
Perspectives on Health and Exercise

Introduction C.Riddoch and J.McKenna SECTION1: PHYSICAL ACTIVITY: A LIFECOURSE PERSPECTIVE Physical Activity and health through the Lifespan C.Boreham and C.Riddoch SECTION 2: PHYSICAL ACTIVITY AND HEALTH RELATIONSHIPS Epidemiology of Activity and Physical Health R.W. Jakes and N.J.Wareham Physical Activity and Mental Health D.Carless and G.Faulkner Objective Measurements of Physical Activity A.Copper Risks of Physical Activity W.van Mechelen SECTION 3: PROMOTING PHYSICAL ACTIVITY Inactivity, A Problem Waiting to be Solved N.Mutrie and C.Woods The Role of Primary Care A.Taylor Promoting Physical Activity through Schools K.Fox and J.Harris The Environment: The Greatest Barrier? J.Kerr, F.Ewes and D.Carroll SECTION 4: THE BIOLOGY AND ANTHROPOLOGY OF PHYSICAL ACTIVITY Biological Aspects of Physical Activity and Health A.Vivu and M.Harro Bibliography Index
Comment: Reflections on the Deconfounder

The aim of this comment (set to appear in a formal discussion in JASA) is to draw out some conclusions from an extended back-and-forth I have had with Wang and Blei regarding the deconfounder method proposed in "The Blessings of Multiple Causes" [arXiv:1805.06826]. I will make three points here. First, in my role as the critic in this conversation, I will summarize some arguments about the lack of causal identification in the bulk of settings where the "informal" message of the paper suggests that the deconfounder could be used. This is a point that is discussed at length in D'Amour 2019 [arXiv:1902.10286], which motivated the results concerning causal identification in Theorems 6--8 of "Blessings". Second, I will argue that adding parametric assumptions to the working model in order to obtain identification of causal parameters (a strategy followed in Theorem 6 and in the experimental examples) is a risky strategy, and should only be done when extremely strong prior information is available. Finally, I will consider the implications of the nonparametric identification results provided for a narrow, but non-trivial, set of causal estimands in Theorems 7 and 8. I will highlight that these results may be even more interesting from the perspective of detecting causal identification from observed data, under relatively weak assumptions about confounders.
Plasma-assisted molecular beam epitaxy of NiO on GaN(00.1)

The growth of NiO on GaN(00.1) substrates by plasma-assisted molecular beam epitaxy under oxygen rich conditions was investigated at growth temperatures between 100 $^{\circ}$C and 850 $^{\circ}$C. Epitaxial growth of NiO(111) with two rotational domains, with epitaxial relation $\normalsize{}\mathrm{\mathrm{\mathrm{NiO}(1\bar{\mathrm{{1}}}0)}\:||\:\mathrm{\mathrm{GaN}(11.0)}}$ and $\mathrm{\mathrm{\mathrm{NiO}\mathrm{(10\bar{\mathrm{{1}}})}\:||\:\mathrm{GaN(11.0)}}}$, was observed by X-ray diffraction (XRD) and confirmed by in-situ reflection high-energy electron diffraction as well as transmission electron microscopy (TEM) and electron backscatter diffraction. With respect to the high lattice mismatch of 8.1 % and a measured low residual tensile layer strain, growth by lattice matching epitaxy or domain matching epitaxy is discussed. The morphology measured by atomic force microscopy showed a grainy surface, probably arising from the growth by the columnar rotational domains visible in TEM micrographs. The domain sizes measured by AFM and TEM increase with the growth temperature, indicating an increasing surface diffusion length. Growth at 850 $^{\circ}$C, however, involved local decomposition of the GaN substrate that lead to an interfacial $\mathrm{\beta}$-Ga$\mathrm{_{2}}$O$\mathrm{_{3}}$($\bar{\mathrm{{2}}}$01) layer and a high NiO surface roughness. Raman mesurements of the quasi-forbidden one-phonon peak indicate increasing layer quality (decreasing defect density) with increasing growth temperature. The results above suggest optimum growth temperatures around 700 $^{\circ}$C for high layer and interface quality.
Glueball scattering cross section in lattice 
SU(2)
 Yang-Mills theory

We calculate the scattering cross section between $0^{++}$ glueballs of the SU(2) Yang-Mills theory on lattice using the indirect (HAL QCD) method. We employ the cluster-decomposition error reduction technique and use all space-time symmetries to improve the signal. The relation between the interglueball cross section and the scale parameter $\Lambda$ is determined as $\sigma_{\phi \phi}$ = (3.5 - 8.0) $\Lambda^{-2}$ (stat.+sys.). From the observational constraints of galactic collisions, we obtain the lower bound $\Lambda$ > 60 MeV. We also discuss the naturalness of the Yang-Mills theory as the theory explaining dark matter.
Residual Switching Network for Portfolio Optimization

This paper studies deep learning methodologies for portfolio optimization in the US equities market. We present a novel residual switching network that can automatically sense changes in market regimes and switch between momentum and reversal predictors accordingly. The residual switching network architecture combines two separate residual networks (ResNets), namely a switching module that learns stock market conditions, and the main module that learns momentum and reversal predictors. We demonstrate that over-fitting noisy financial data can be controlled with stacked residual blocks and further incorporating the attention mechanism can enhance powerful predictive properties. Over the period 2008 to H12017, the residual switching network (Switching-ResNet) strategy verified superior out-of-sample performance with an average annual Sharpe ratio of 2.22, compared with an average annual Sharpe ratio of 0.81 for the ANN-based strategy and 0.69 for the linear model.
The Pristine survey – VII. A cleaner view of the Galactic outer halo using blue horizontal branch stars

We use the Pristine survey CaHK narrow-band photometry, combined with the Sloan Digital Sky Survey (SDSS)ugr photometry, to provide a cleaner sample of blue horizontal branch stars in the Galactic halo out to large distances. We demonstrate a completeness of 91 per cent and a purity of 93 per cent with respect to available spectroscopic classifications. We subsequently use our new clean sample of these standard candles to investigate the substructure in the Galactic halo over the Pristine footprint. Among other features, this allows for a careful tracing of multiple parts of the Sagittarius stream, providing a measurement independent from other tracers used and reaching larger distances. Moreover, we demonstrate with this clean and complete sample that the halo follows a density profile with a negative power-law slope of 3.5–4.0. As the relatively shallow SDSS u band is the limiting factor in this technique, we foresee large potential for combining Pristine survey photometry with the much deeper u-band photometry from the Canada–France–Imaging Survey.
Controllable splitting dynamics of a doubly quantized vortex in a ring-shaped condensate

We prepared a ring-shaped Bose–Einstein condensate with quantized circulation by imprinting a linear azimuthal phase. A doubly quantized vortex (DQV) can be generated and exist stably in the middle of the condensate cloud after the system is released into a rotationally symmetric harmonic trap. For an asymmetric trap with a small degree of anisotropy the generated DQV initially splits into two singly quantized vortices and revives again but eventually evolves into two unit vortices due to the dynamic instability. For the degree of anisotropy above a critical value, the DQV is extremely unstable and decays rapidly into two singlet vortices. The geometry-dependent lifetime of the DQV and vortex-induced excitations are also discussed intensively.
Sampling and resolution characteristics in reduced order models of shallow water equations: Intrusive vs nonintrusive

We investigate the sensitivity of reduced order models (ROMs) to training data spatial resolution as well as sampling rate. In particular, we consider proper orthogonal decomposition (POD), coupled with Galerkin projection (POD‐GP), as an intrusive ROM technique. For nonintrusive ROMs, we consider two frameworks. The first is using dynamic mode decomposition (DMD), and the second is based on artificial neural networks (ANNs). For ANN, we utilized a residual deep neural network, and for DMD we have studied two versions of DMD approaches; one with hard thresholding and the other with sorted bases selection. Also, we highlight the differences between mean subtracting the data (centering) and using the data without mean subtraction. We tested these ROMs using a system of 2D shallow water equations for four different numerical experiments, adopting combinations of sampling rates and spatial resolutions. For these cases, we found that the DMD basis obtained with hard thresholding is sensitive to sampling rate. The sorted DMD algorithm helps to mitigate this problem and yields more stabilized and converging solution. Furthermore, we demonstrate that both DMD approaches without mean subtraction provide significantly more accurate results than DMD with mean subtracting the data. On the other hand, POD is relatively insensitive to sampling rate and yields better representation of the flow field. Meanwhile, spatial resolution has little effect on both POD and DMD performances. Numerical results reveal that an ANN on POD subspace (POD‐ANN) performs remarkably better than POD‐GP and DMD in capturing system dynamics, even with a small number of modes.
A 3D Radiation Hydrodynamic AGB Binary Model

The origin of chemically peculiar stars and nonzero eccentricity in evolved close binaries have been long-standing problems in binary stellar evolution. Answers to these questions may trace back to an intense mass transfer during the asymptotic-giant-branch (AGB) binary phase. In this work, we use AstroBEAR to solve the 3D radiation hydrodynamic equations and calculate the mass transfer rate in AGB binaries that undergo the wind-Roche-lobe overflow or Bondi–Hoyle–Lyttleton (BHL) accretion. MESA produces the density and temperature of the boundary condition of the AGB star. To improve the resolution of the dynamics of a circumbinary disk, we implement an azimuthal angle dependent 3D radiation transfer. We consider optically thin cooling and obtain the number density of the coolants by solving Saha equations. One of the goals of this work is to illustrate the transition from the wind-Roche-lobe overflow to BHL accretion. Both circumbinary disks and spiral structure outflows can appear in the simulations. Circumbinary disks may form when the optical thickness in the equatorial region increases. The increase of the optical thickness is due to the deflected wind. The resulting mass transfer efficiency in our models is up to a factor of 8 times higher than what the standard BHL accretion scenario predicts, and the outflow gains up to 91% of its initial angular momentum when it reaches 1.3 binary separations. Consequently, some AGB binaries may undergo orbit shrinkage, and some will expand. The high mass transfer efficiency is closely related to the presence of the circumbinary disks.
Machine learning and big scientific data

This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory (RAL) site at Harwell near Oxford. Such ‘Big Scientific Data’ comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility and the UK's Central Laser Facility. Increasingly, scientists are now required to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now used the deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, it has been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems? After a brief review of some initial applications of machine learning at the RAL, we focus on challenges and opportunities for AI in advancing materials science. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from several different scientific domains. We conclude with some initial examples of our ‘scientific machine learning’ benchmark suite and of the research challenges these benchmarks will enable. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.
Graph Embedding VAE: A Permutation Invariant Model of Graph Structure

Generative models of graph structure have applications in biology and social sciences. The state of the art is GraphRNN, which decomposes the graph generation process into a series of sequential steps. While effective for modest sizes, it loses its permutation invariance for larger graphs. Instead, we present a permutation invariant latent-variable generative model relying on graph embeddings to encode structure. Using tools from the random graph literature, our model is highly scalable to large graphs with likelihood evaluation and generation in $O(|V | + |E|)$.