Exact two-component TDDFT with simple two-electron picture-change corrections: X-ray absorption spectra near L- and M-edges of four-component quality at two-component cost

X-ray absorption spectroscopy (XAS) has gained popularity in recent years as it probes matter with high spatial and elemental sensitivity. However, the theoretical modelling of XAS is a challenging task since XAS spectra feature a fine structure due to scalar (SC) and spin-orbit (SO) relativistic effects, in particular near L and M absorption edges. While full four-component (4c) calculations of XAS are nowadays feasible, there is still interest in developing approximate relativistic methods that enable XAS calculations at the two-component (2c) level while maintaining the accuracy of the parent 4c approach. In this article we present theoretical and numerical insights into two simple yet accurate 2c approaches based on an (extended) atomic mean-field exact two-component Hamiltonian framework, (e)amfX2C, for the calculation of XAS using linear eigenvalue and damped-response time-dependent density functional theory (TDDFT). In contrast to the commonly used one-electron X2C (1eX2C) Hamiltonian, both amfX2C and eamfX2C account for the SC and SO two-electron and exchange-correlation picture-change (PC) effects that arise from the X2C transformation. As we demonstrate on L- and M-edge XAS spectra of transition metal and actinide compounds, the absence of PC corrections in the 1eX2C approximation results in a substantial overestimatation of SO splittings, whereas (e)amfX2C Hamiltonians reproduce all essential spectral features such as shape, position, and SO splitting of the 4c references in excellent agreement, while offering significant computational savings. Therefore, the (e)amfX2C PC correction models presented here constitute reliable relativistic 2c quantum-chemical approaches for modelling XAS

Psychiatric admissions from crisis resolution teams in Norway: a prospective multicentre study

Background Crisis resolution teams (CRTs) provide intensive alternative care to hospital admission for patients with mental health crises. The aims of this study were to describe the proportions and characteristics of patients admitted to in-patient wards from CRTs, to identify any differences in admission practices between CRTs, and to identify predictors of admissions from CRTs. Methods A naturalistic prospective multicentre design was used to study 680 consecutive patients under the care of eight CRTs in Norway over a 3-month period in 2005/2006. Socio-demographic and clinical data were collected on the patients, and on the organization and operation of the CRTs. Logistic regression analysis for hierarchical data was used to test potential predictors of admission at team and patient level. Results One hundred and forty-six patients (21.5%) were admitted to in-patient wards. There were significant differences in admission rates between the CRTs. The likelihood of being admitted to an in-patient ward was significantly lower for patients treated by CRTs that operated during extended opening hours than CRTs that operated during office hours only. Those most likely to be admitted were patients with psychotic symptoms, suicidal risk, and a prior history of admissions. Conclusions Extended opening hours may help CRTs to prevent more admissions for patients with moderately severe and relapsing mental illnesses. Patients with severe psychosis seem to be difficult to treat in the community by Norwegian CRTs even with extended opening hours

Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI

Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality. Consequently, the design of optimal sampling patterns for these k-space coefficients has received significant attention, with many CS MRI methods exploiting variable-density probability distributions. Realizing that an optimal sampling pattern may depend on the downstream task (e.g. image reconstruction, segmentation, or classification), we here propose joint learning of both task-adaptive k-space sampling and a subsequent model-based proximal-gradient recovery network. The former is enabled through a probabilistic generative model that leverages the Gumbel-softmax relaxation to sample across trainable beliefs while maintaining differentiability. The proposed combination of a highly flexible sampling model and a model-based (sampling-adaptive) image reconstruction network facilitates exploration and efficient training, yielding improved MR image quality compared to other sampling baselines

The Q/U Imaging Experiment: Polarization Measurements of the Galactic Plane at 43 and 95 GHz

We present polarization observations of two Galactic plane fields centered on Galactic coordinates (l, b) = (0°, 0°) and (329°, 0°) at both Q (43 GHz) and W bands (95 GHz), covering between 301 and 539 square degrees depending on frequency and field. These measurements were made with the QUIET instrument between 2008 October and 2010 December, and include a total of 1263 hr of observations. The resulting maps represent the deepest large-area Galactic polarization observations published to date at the relevant frequencies with instrumental rms noise varying between 1.8 and 2.8 μK deg, 2.3–6 times deeper than corresponding WMAP and Planck maps. The angular resolution is 27!3 and 12!8 FWHM at Q and W bands, respectively. We find excellent agreement between the QUIET and WMAP maps over the entire fields, and no compelling evidence for significant residual instrumental systematic errors in either experiment, whereas the Planck 44 GHz map deviates from these in a manner consistent with reported systematic uncertainties for this channel. We combine QUIET and WMAP data to compute inverse-variance-weighted average maps, effectively retaining small angular scales from QUIET and large angular scales from WMAP. From these combined maps, we derive constraints on several important astrophysical quantities, including a robust detection of polarized synchrotron spectral index steepening of ≈0.2 off the plane, as well as the Faraday rotation measure toward the Galactic center (RM = −4000 ± 200 rad m^(−2)), all of which are consistent with previously published results. Both the raw QUIET and the co-added QUIET+WMAP maps are made publicly available together with all necessary ancillary information

A False Start in the Race Against Doping in Sport: Concerns With Cycling’s Biological Passport

Professional cycling has suffered from a number of doping scandals. The sport’s governing bodies have responded by implementing an aggressive new antidoping program known as the biological passport. Cycling’s biological passport marks a departure from traditional antidoping efforts, which have focused on directly detecting prohibited substances in a cyclist’s system. Instead, the biological passport tracks biological variables in a cyclist’s blood and urine over time, monitoring for fluctuations that are thought to indirectly reveal the effects of doping. Although this method of indirect detection is promising, it also raises serious legal and scientific concerns. Since its introduction, the cycling community has debated the reliability of indirect biological-passport evidence and the clarity, consistency, and transparency of its use in proving doping violations. Such uncertainty undermines the legitimacy of finding cyclists guilty of doping based on this indirect evidence alone. Antidoping authorities should address these important concerns before continuing to pursue doping sanctions against cyclists solely on the basis of their biological passports

Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging

Limitations on bandwidth and power consumption impose strict bounds on data rates of diagnostic imaging systems. Consequently, the design of suitable (i.e. task- and data-aware) compression and reconstruction techniques has attracted considerable attention in recent years. Compressed sensing emerged as a popular framework for sparse signal reconstruction from a small set of compressed measurements. However, typical compressed sensing designs measure a (non)linearly weighted combination of all input signal elements, which poses practical challenges. These designs are also not necessarily task-optimal. In addition, real-time recovery is hampered by the iterative and time-consuming nature of sparse recovery algorithms. Recently, deep learning methods have shown promise for fast recovery from compressed measurements, but the design of adequate and practical sensing strategies remains a challenge. Here, we propose a deep learning solution termed Deep Probabilistic Sub-sampling (DPS), that learns a task-driven sub-sampling pattern, while jointly training a subsequent task model. Once learned, the task-based sub-sampling patterns are fixed and straightforwardly implementable, e.g. by non-uniform analog-to-digital conversion, sparse array design, or slow-time ultrasound pulsing schemes. The effectiveness of our framework is demonstrated in-silico for sparse signal recovery from partial Fourier measurements, and in-vivo for both anatomical image and tissue-motion (Doppler) reconstruction from sub-sampled medical ultrasound imaging data

Active-distributed temperature sensing to continuously quantify vertical flow in boreholes

We show how a distributed borehole flowmeter can be created from armored Fiber Optic cables with the Active-Distributed Temperature Sensing (A-DTS) method. The principle is that in a flowing fluid, the difference in temperature between a heated and unheated cable is a function of the fluid velocity. We outline the physical basis of the methodology and report on the deployment of a prototype A-DTS flowmeter in a fractured rock aquifer. With this design, an increase in flow velocity from 0.01 to 0.3 m s−1 elicited a 2.5°C cooling effect. It is envisaged that with further development this method will have applications where point measurements of borehole vertical flow do not fully capture combined spatiotemporal dynamics

Monitoring the recovery-stress states of athletes: Psychometric properties of the Acute Recovery and Stress Scale and Short Recovery and Stress Scale among Dutch and Flemish Athletes

The Acute Recovery and Stress Scale (ARSS) and the Short Recovery and Stress Scale (SRSS) are recently-introduced instruments to monitor recovery and stress processes in athletes. In this study, our aims were to replicate and extend previous psychometric assessments of the instruments, by incorporating recovery and stress dimensions into one model. Therefore, we conducted five confirmatory factor analyses (CFA) and determined structural validity, internal consistency, cross-cultural validity, and construct validity. Dutch and Flemish athletes (N=385, 213 females, 170 males, 2 others, 21.03±5.44 years) completed the translated ARSS and SRSS, the Recovery Stress Questionnaire for Athletes (RESTQ-Sport-76), and information on their last training. There was a good model fit for the replicated CFA, sub-optimal model fit for the models that incorporated recovery and stress into one model, and satisfactory internal consistency (α=.75 – .87). The correlations within and between the ARSS and SRSS, as well as between the ARSS/SRSS and the RESTQ-Sport-76 (r=.31 – -.77 for the ARSS, r=.28 – -.63 for the SRSS) and information of their last training also supported construct validity. The combined findings support the use of the ARSS and SRSS to assess stress and recovery in sports-related research and practice

Dehazing Ultrasound using Diffusion Models

Echocardiography has been a prominent tool for the diagnosis of cardiac disease. However, these diagnoses can be heavily impeded by poor image quality. Acoustic clutter emerges due to multipath reflections imposed by layers of skin, subcutaneous fat, and intercostal muscle between the transducer and heart. As a result, haze and other noise artifacts pose a real challenge to cardiac ultrasound imaging. In many cases, especially with difficult-to-image patients such as patients with obesity, a diagnosis from B-Mode ultrasound imaging is effectively rendered unusable, forcing sonographers to resort to contrast-enhanced ultrasound examinations or refer patients to other imaging modalities. Tissue harmonic imaging has been a popular approach to combat haze, but in severe cases is still heavily impacted by haze. Alternatively, denoising algorithms are typically unable to remove highly structured and correlated noise, such as haze. It remains a challenge to accurately describe the statistical properties of structured haze, and develop an inference method to subsequently remove it. Diffusion models have emerged as powerful generative models and have shown their effectiveness in a variety of inverse problems. In this work, we present a joint posterior sampling framework that combines two separate diffusion models to model the distribution of both clean ultrasound and haze in an unsupervised manner. Furthermore, we demonstrate techniques for effectively training diffusion models on radio-frequency ultrasound data and highlight the advantages over image data. Experiments on both \emph{in-vitro} and \emph{in-vivo} cardiac datasets show that the proposed dehazing method effectively removes haze while preserving signals from weakly reflected tissue.Comment: 10 pages, 11 figures, preprint IEEE submissio