Integrable semi-discretization of the coupled nonlinear Schr\"{o}dinger equations

A system of semi-discrete coupled nonlinear Schr\"{o}dinger equations is studied. To show the complete integrability of the model with multiple components, we extend the discrete version of the inverse scattering method for the single-component discrete nonlinear Schr\"{o}dinger equation proposed by Ablowitz and Ladik. By means of the extension, the initial-value problem of the model is solved. Further, the integrals of motion and the soliton solutions are constructed within the framework of the extension of the inverse scattering method.Comment: 27 pages, LaTeX2e (IOP style

First-Order Phase Transition in Potts Models with finite-range interactions

We consider the $Q$-state Potts model on $\mathbb Z^d$, $Q\ge 3$, $d\ge 2$, with Kac ferromagnetic interactions and scaling parameter \ga. We prove the existence of a first order phase transition for large but finite potential ranges. More precisely we prove that for \ga small enough there is a value of the temperature at which coexist $Q+1$ Gibbs states. The proof is obtained by a perturbation around mean-field using Pirogov-Sinai theory. The result is valid in particular for $d=2$, Q=3, in contrast with the case of nearest-neighbor interactions for which available results indicate a second order phase transition. Putting both results together provides an example of a system which undergoes a transition from second to first order phase transition by changing only the finite range of the interaction.Comment: Soumis pour publication a Journal of statistical physics - version r\'{e}vis\'{e}

Quality control for more reliable integration of deep learning-based image segmentation into medical workflows

Machine learning algorithms underpin modern diagnostic-aiding software, whichhas proved valuable in clinical practice, particularly in radiology. However,inaccuracies, mainly due to the limited availability of clinical samples fortraining these algorithms, hamper their wider applicability, acceptance, andrecognition amongst clinicians. We present an analysis of state-of-the-artautomatic quality control (QC) approaches that can be implemented within thesealgorithms to estimate the certainty of their outputs. We validated the mostpromising approaches on a brain image segmentation task identifying whitematter hyperintensities (WMH) in magnetic resonance imaging data. WMH are acorrelate of small vessel disease common in mid-to-late adulthood and areparticularly challenging to segment due to their varied size, anddistributional patterns. Our results show that the aggregation of uncertaintyand Dice prediction were most effective in failure detection for this task.Both methods independently improved mean Dice from 0.82 to 0.84. Our workreveals how QC methods can help to detect failed segmentation cases andtherefore make automatic segmentation more reliable and suitable for clinicalpractice.<br

A deficit of spatial remapping in constructional apraxia after right-hemisphere stroke

This Article is provided by the Brunel Open Access Publising Fund - Copyright @ 2010 Oxford University PressConstructional apraxia refers to the inability of patients to copy accurately drawings or three-dimensional constructions. It is a common disorder after right parietal stroke, often persisting after initial problems such as visuospatial neglect have resolved. However, there has been very little experimental investigation regarding mechanisms that might contribute to the syndrome. Here, we examined whether a key deficit might be failure to integrate visual information correctly from one fixation to the next. Specifically, we tested whether this deficit might concern remapping of spatial locations across saccades. Right-hemisphere stroke patients with constructional apraxia were compared to patients without constructional problems and neurologically healthy controls. Participants judged whether a pattern shifted position (spatial task) or changed in pattern (non-spatial task) across two saccades, compared to a control condition with an equivalent delay but without intervening eye movements. Patients with constructional apraxia were found to be significantly impaired in position judgements with intervening saccades, particularly when the first saccade of the sequence was to the right. The importance of these remapping deficits in constructional apraxia was confirmed through a highly significant correlation between saccade task performance and constructional impairment on standard neuropsychological tasks. A second study revealed that even single saccades to the right can impair constructional apraxia patients’ perception of location shifts. These data are consistent with the view that rightward eye movements result in loss of remembered spatial information from previous fixations, presumably due to constructional apraxia patients’ damage to the right-hemisphere regions involved in remapping locations across saccades. These findings provide the first evidence for a deficit in remapping visual information across saccades underlying right-hemisphere constructional apraxia.European Commission Marie Curie Intra-European Fellowship (011457 to C.R.) and a Wellcome Trust Senior Fellowship (to M.H.)

Integrable discretizations of derivative nonlinear Schroedinger equations

We propose integrable discretizations of derivative nonlinear Schroedinger (DNLS) equations such as the Kaup-Newell equation, the Chen-Lee-Liu equation and the Gerdjikov-Ivanov equation by constructing Lax pairs. The discrete DNLS systems admit the reduction of complex conjugation between two dependent variables and possess bi-Hamiltonian structure. Through transformations of variables and reductions, we obtain novel integrable discretizations of the nonlinear Schroedinger (NLS), modified KdV (mKdV), mixed NLS, matrix NLS, matrix KdV, matrix mKdV, coupled NLS, coupled Hirota, coupled Sasa-Satsuma and Burgers equations. We also discuss integrable discretizations of the sine-Gordon equation, the massive Thirring model and their generalizations.Comment: 24 pages, LaTeX2e (IOP style), final versio

FHIR-DHP: A standardized clinical data harmonisation pipeline for scalable AI application deployment

Background Increasing digitalisation in the medical domain gives rise to large amounts of healthcare data which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to non-standardised data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the healthcare system. Despite the existence of standardised data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remains limited.Objective We developed a data harmonisation pipeline (DHP) for clinical data sets relying on the common FHIR data standard.Methods We validated the performance and usability of our FHIR-DHP with data from the MIMIC IV database including > 40,000 patients admitted to an intensive care unit.Results We present the FHIR-DHP workflow in respect of transformation of “raw” hospital records into a harmonised, AI-friendly data representation. The pipeline consists of five key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonised data into the patient-model database and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records.Conclusions Our approach enables scalable and needs-driven data modelling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step towards increasing cooperation, interoperability and quality of patient care in the clinical routine and for medical research

Patient-reported outcomes data in patients with psoriatic arthritis from a randomised trial of etanercept and methotrexate as monotherapy or in combination.

OBJECTIVES: We examined patient-reported outcomes (PROs) in The Study of Etanercept And Methotrexate in Patients with Psoriatic Arthritis (PsA); a 48-week, phase 3, randomised controlled trial that compared outcomes with methotrexate (MTX) monotherapy, etanercept monotherapy, and MTX+ etanercept in patients with PsA. METHODS: Efficacy endpoints included: mean changes from baseline and proportion of patients who reported improvements≥minimal clinically important difference (MCID) at week 24 in treatment groups for Health Assessment Questionnaire-Disability Index, Patient Global Assessment (PtGA), Patient Global Assessment of Joint Pain (PtGAJP) and Medical Outcomes Study Short Form-36 Questionnaire (SF-36) Physical Component Summary (PCS), and Mental Component Summary, and eight domain scores. PROs were analysed as reported (observed), without multiplicity adjustment; therefore, p values are descriptive. RESULTS: At week 24, patients receiving etanercept monotherapy or MTX+ etanercept combination reported greater improvements (p≤0.05) in PtGA, PtGAJP and SF-36 PCS scores compared with those receiving MTX monotherapy. Compared with MTX monotherapy, higher proportions of patients receiving etanercept monotherapy and combination therapy reported improvements≥MCID in PtGA (etanercept vs MTX, p=0.005) and PtGAJP (MTX +etanercept vs MTX, p=0.038). Across PROs, proportions of patients reporting scores≥age and gender-matched normative values at week 24 ranged from 20.8% to 51.0% with MTX monotherapy, 30.9% to 48.8% with etanercept monotherapy, and 30.6% to 52.3% with MTX+ etanercept combination. CONCLUSIONS: Patients receiving etanercept monotherapy or MTX+ etanercept reported greater improvements from baseline in several PROs compared with those receiving MTX monotherapy. PROs should be incorporated in discussions between patients and clinicians regarding their treatment choices as they can help determine which treatments are more beneficial in patients with PsA

Convolutional neural network stacking for medical image segmentation in CT scans

Computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs). The main challenges in handling CT scans with CNN are the scale of data (large range of Hounsfield Units) and the processing of the slices. In this paper, we consider a framework, which addresses these demands regarding the data preprocessing, the data augmentation, and the CNN architecture itself. For this purpose, we present a data preprocessing and an augmentation method tailored to CT data. We evaluate and compare different input dimensionalities and two different CNN architectures. One of the architectures is a modified U-Net and the other a modified Mixed-Scale Dense Network (MS-D Net). Thus, we compare dilated convolutions for parallel multi-scale processing to the U-Net approach with traditional scaling operations based on the different input dimensionalities. Finally, we combine a set of 3D modified MS-D Nets and a set of 2D modified U-Nets as a stacked CNN-model to combine the different strengths of both model

Machine learning applied to ambulatory blood pressure monitoring: a new tool to diagnose autonomic failure?

BACKGROUND: Autonomic failure (AF) complicates Parkinson’s disease (PD) in one-third of cases, resulting in complex blood pressure (BP) abnormalities. While autonomic testing represents the diagnostic gold standard for AF, accessibility to this examination remains limited to a few tertiary referral centers. OBJECTIVE: The present study sought to investigate the accuracy of a machine learning algorithm applied to 24-h ambulatory BP monitoring (ABPM) as a tool to facilitate the diagnosis of AF in patients with PD. METHODS: Consecutive PD patients naïve to vasoactive medications underwent 24 h-ABPM and autonomic testing. The diagnostic accuracy of a Linear Discriminant Analysis (LDA) model exploiting ABPM parameters was compared to autonomic testing (as per a modified version of the Composite Autonomic Symptom Score not including the sudomotor score) in the diagnosis of AF. RESULTS: The study population consisted of n = 80 PD patients (33% female) with a mean age of 64 ± 10 years old and disease duration of 6.2 ± 4 years. The prevalence of AF at the autonomic testing was 36%. The LDA model showed 91.3% accuracy (98.0% specificity, 79.3% sensitivity) in predicting AF, significantly higher than any of the ABPM variables considered individually (hypotensive episodes = 82%; reverse dipping = 79%; awakening hypotension = 74%). CONCLUSION: LDA model based on 24-h ABPM parameters can effectively predict AF, allowing greater accessibility to an accurate and easy to administer test for AF. Potential applications range from systematic AF screening to monitoring and treating blood pressure dysregulation caused by PD and other neurodegenerative disorders

Behavioural signs of pain in cats: an expert consensus

To identify where a consensus can be reached between veterinary experts in feline medicine on the core signs sufficient for pain (sufficient to indicate pain when they occur, but not necessarily present in all painful conditions) and necessary for pain (necessary in the presence of pain, but not always indicative of pain). Methods A modified Delphi technique was used, consisting of four rounds of questions and evaluation using nineteen participants during the period December 2014 and May 2015. Agreement was considered to be established when 80% of the experts concurred with the same opinion. Results Twenty-five signs were considered sufficient to indicate pain, but no single sign was considered necessary for it. Discussion Further studies are needed to evaluate the validity of these 25 behavioural signs if a specific pain assessment tool is to be developed that is capable of assessing pain in cats based on observational methods alone. The signs reported here may nonetheless help both vets and owners form an initial evaluation of the pain status of cats in their care