Hyperkähler cones and instantons on quaternionic Kähler manifolds

We present a novel approach to the study of Yang-Mills instantons on quaternionic Kähler manifolds, based on an extension of the harmonic space method of constructing instantons on hyperk\"ahler manifolds. Our results establish a bijection between local equivalence classes of instantons on quaternionic Kähler manifolds M and equivalence classes of certain holomorphic maps on an appropriate SL_2(C)-bundle over the Swann bundle of M

Bridging the Silos: The Effects of Including Social Workers in Integrated Healthcare Teams in the Treatment of Chronic Pain

An exploratory study of how the inclusion of social workers on integrated treatment teams affects the satisfaction of chronic pain patients. This study utilizes a mixed methods approach: interviews with social workers currently working in integrated healthcare teams as well as anonymous survey data collected from people who identified as having experienced chronic pain in order to provide as much initial data as possible. A content analysis reveals qualitative themes including patient advocacy, the power of integrated healthcare, and the importance of the mind/body connection in integrated healthcare. Qualitative and quantitative both find evidence of a lack of social worker visibility in medical settings which impedes the availability of data regarding social worker effect on chronic pain patient satisfaction. Implications for social work practice and future research are discussed

Methods for developing a machine learning framework for precise 3D domain boundary prediction at base-level resolution

High-throughput chromosome conformation capture technology (Hi-C) has revealed extensive DNA looping and folding into discrete 3D domains. These include Topologically Associating Domains (TADs) and chromatin loops, the 3D domains critical for cellular processes like gene regulation and cell differentiation. The relatively low resolution of Hi-C data (regions of several kilobases in size) prevents precise mapping of domain boundaries by conventional TAD/loop-callers. However, high resolution genomic annotations associated with boundaries, such as CTCF and members of cohesin complex, suggest a computational approach for precise location of domain boundaries. We developed preciseTAD, an optimized machine learning framework that leverages a random forest model to improve the location of domain boundaries. Our method introduces three concepts - shifted binning, distance-type predictors, and random under-sampling - which we use to build classification models for predicting boundary regions. The algorithm then uses density-based clustering (DBSCAN) and partitioning around medoids (PAM) to extract the most biologically meaningful domain boundary from models trained on high-resolution genome annotation data and boundaries from low-resolution Hi-C data. We benchmarked our method against a popular TAD-caller and a novel chromatin loop prediction algorithm. Boundaries predicted by preciseTAD were more enriched for known molecular drivers of 3D chromatin including CTCF, RAD21, SMC3, and ZNF143. preciseTAD-predicted boundaries were more conserved across cell lines, highlighting their higher biological significance. Additionally, models pre-trained in one cell line accurately predict boundaries in another cell line. Using cell line-specific genomic annotations, the pre-trained models enable detecting domain boundaries in cells without Hi-C data. The research presented provides a unified approach for precisely predicting domain boundaries. This improved precision will provide insight into the association between genomic regulators and the 3D genome organization. Furthermore, our methods will provide researchers with flexible and easy-to-use tools to continue to annotate the 3D structure of the human genome without relying on costly high resolution Hi-C data. The preciseTAD R package and supplementary ExperimentHub package, preciseTADhub, are available on Bioconductor (version 3.13; https://bioconductor.org/packages/preciseTAD/; https://bioconductor.org/packages/preciseTADhub/)

Bridging the Silos: The Effects of Including Social Workers in Integrated Healthcare Teams in the Treatment of Chronic Pain

An exploratory study of how the inclusion of social workers on integrated treatment teams affects the satisfaction of chronic pain patients. This study utilizes a mixed methods approach: interviews with social workers currently working in integrated healthcare teams as well as anonymous survey data collected from people who identified as having experienced chronic pain in order to provide as much initial data as possible. A content analysis reveals qualitative themes including patient advocacy, the power of integrated healthcare, and the importance of the mind/body connection in integrated healthcare. Qualitative and quantitative both find evidence of a lack of social worker visibility in medical settings which impedes the availability of data regarding social worker effect on chronic pain patient satisfaction. Implications for social work practice and future research are discussed

Antibiotic-Induced Thrombocytopenia in the ICU: Case Report of a Diagnostic Challenge

Thrombocytopenia is the most common coagulation problem in ICU patients and is an independent predictor of death among critically ill patients. The differential diagnosis of acute thrombocytopenia in an ICU patient is extensive. After eliminating the more common etiologies, drug-induced thrombocytopenia (DITP) should be considered as an often overlooked yet easily reversible cause of thrombocytopenia. Due to a lack of distinguishing clinical features and numerous other possible etiologies, diagnosis is often complex, requiring a multi-step approach. We discuss the extensive workup of DITP in the context of this unusual case presentation

“Sounds good, but… what is it?” an introduction to outcome measurement from a music therapy perspective

“Sounds good, but… what is it?” This is a common reaction to outcome measurement by music therapy practitioners and researchers who are less familiar with its meanings and practices. Given the prevailing evidence-based practice movement, outcome measurement does ‘sound good’. Some practitioners and researchers, however, have a limited or unclear understanding of what outcome measurement includes; particularly with respect to outcome measures and related terminology around their use. Responding to the “what is it?” question, this article provides an introduction to such terminology. It explores what outcome measures are and outlines characteristics related to their forms, uses and selection criteria. While pointing to some debates regarding outcome measurement, including its philosophical underpinnings, this introduction seeks to offer a useful platform for a critical and contextual understanding of the potential use of outcome measures in music therapy

A scalable and fast artificial neural network syndrome decoder for surface codes

Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing. When operated as stabiliser codes, surface code computations consist of a syndrome decoding step where measured stabiliser operators are used to determine appropriate corrections for errors in physical qubits. Decoding algorithms have undergone substantial development, with recent work incorporating machine learning (ML) techniques. Despite promising initial results, the ML-based syndrome decoders are still limited to small scale demonstrations with low latency and are incapable of handling surface codes with boundary conditions and various shapes needed for lattice surgery and braiding. Here, we report the development of an artificial neural network (ANN) based scalable and fast syndrome decoder capable of decoding surface codes of arbitrary shape and size with data qubits suffering from a variety of noise models including depolarising errors, biased noise, and spatially inhomogeneous noise. Based on rigorous training over 50 million random quantum error instances, our ANN decoder is shown to work with code distances exceeding 1000 (more than 4 million physical qubits), which is the largest ML-based decoder demonstration to-date. The established ANN decoder demonstrates an execution time in principle independent of code distance, implying that its implementation on dedicated hardware could potentially offer surface code decoding times of O($\mu$sec), commensurate with the experimentally realisable qubit coherence times. With the anticipated scale-up of quantum processors within the next decade, their augmentation with a fast and scalable syndrome decoder such as developed in our work is expected to play a decisive role towards experimental implementation of fault-tolerant quantum information processing.Comment: 11 pages, 6 figure