bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines
Authors: Schenk Michael , Combarro Elías F , Grossi Michele , Kain Verena , Li Kevin Shing Bruce , Popa Mircea-Marian , Vallecorsa Sofia 
Published: February 2024

Abstract
Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the learning efficiency compared to classical Q-learning with the restriction, however, to discrete state-action space environments. In this paper, the FERL approach is extended to multi-dimensional continuous state-action space environments to open the doors for a broader range of real-world applications. First, free energy-based Q-learning is studied for discrete action spaces, but continuous state spaces and the impact of experience replay on sample efficiency is assessed. In a second step, a hybrid actor-critic (A-C) scheme for continuous state-action spaces is developed based on the deep deterministic policy gradient algorithm combining a classical actor network with a QBM-based critic. The results obtained with quantum annealing (QA), both simulated and with D-Wave QA hardware, are discussed, and the performance is compared to classical reinforcement learning methods. The environments used throughout represent existing particle accelerator beam lines at the European Organisation for Nuclear Research. Among others, the hybrid A-C agent is evaluated on the actual electron beam line of the Advanced Wakefield Experiment (AWAKE).
Deep energy-pressure regression for a thermodynamically consistent EOS model
Authors: Yu Dayou , Shankar Pandey Deep , Hinz Joshua , Mihaylov Deyan , Karasiev Valentin V , Hu S X , Yu Qi 
Published: February 2024

Abstract
In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.
Deep quantum graph dreaming: deciphering neural network insights into quantum experiments
Authors: Jaouni Tareq , Arlt Sören , Ruiz-Gonzalez Carlos , Karimi Ebrahim , Gu Xuemei , Krenn Mario 
Published: February 2024

Abstract
Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI technique called inception or deep dreaming, which has been invented in machine learning for computer vision. We use this technique to explore what neural networks learn about quantum optics experiments. Our story begins by training deep neural networks on the properties of quantum systems. Once trained, we ‘invert’ the neural network—effectively asking how it imagines a quantum system with a specific property, and how it would continuously modify the quantum system to change a property. We find that the network can shift the initial distribution of properties of the quantum system, and we can conceptualize the learned strategies of the neural network. Interestingly, we find that, in the first layers, the neural network identifies simple properties, while in the deeper ones, it can identify complex quantum structures and even quantum entanglement. This is in reminiscence of long-understood properties known in computer vision, which we now identify in a complex natural science task. Our approach could be useful in a more interpretable way to develop new advanced AI-based scientific discovery techniques in quantum physics.
Case report of pulmonary vein isolation in situs inversus totalis
Authors: Stauber Annina , Jeger Raban , Dzemali Omer , Müller Andreas Stephan 
Published: February 2024

Abstract

Background
Situs inversus totalis (SIT) is a rare condition, where all the organs in the body are mirrored. Atrial fibrillation occurs in patients with SIT. We describe the case of pulmonary vein isolation (PVI) in SIT.

Case summary
A patient with atrial fibrillation was referred to our hospital due to palpitations. Diagnosis of atrial fibrillation was made by electrocardiogram. The patient reported to have a SIT that was confirmed. Meticulous preparation was done including a three-dimensional model and radiofrequency PVI was performed successfully. No recurrence of atrial fibrillation was detected until last follow-up 2 years after PVI.

Discussion
Pulmonary vein isolation in SIT can be performed successfully and with excellent long-term result.
Trophic niche drives the evolution of craniofacial shape in Trinidadian guppies
Authors: Matthews David G , Reznick David N , Dial Terry R 
Published: February 2024

Abstract
Diverse clades of fishes adapted to feeding on the benthos repeatedly converge on steep craniofacial profiles and shorter, wider heads. But in an incipient radiation, to what extent is this morphological evolution measurable and can we distinguish the relative genetic vs. plastic effects? We use the Trinidadian guppy (Poecilia reticulata) to test the repeatability of adaptation and the alignment of genetic and environmental effects shaping poecilid craniofacial morphology. We compare wild-caught and common garden lab-reared fish to quantify the genetic and plastic components of craniofacial morphology across 4 populations from 2 river drainage systems (n = 56 total). We first use micro-computed tomography to capture 3D morphology, then place both landmarks and semilandmarks to perform size-corrected 3D morphometrics and quantify shape space. We find a measurable, significant, and repeatable divergence in craniofacial shape between high-predation invertivore and low-predation detritivore populations. As predicted from previous examples of piscine adaptive trophic divergence, we find increases in head slope and craniofacial compression among the benthic detritivore foragers. Furthermore, the effects of environmental plasticity among benthic detritivores produce exaggerated craniofacial morphological change along a parallel axis to genetic morphological adaptation from invertivore ancestors. Overall, many of the major patterns of benthic-limnetic craniofacial evolution appear convergent among disparate groups of teleost fishes.
Exponential input-to-state stability for Lur’e systems via Integral Quadratic Constraints and Zames–Falb multipliers
Authors: Drummond Ross , Guiver Chris , Turner Matthew C 
Published: February 2024

Abstract
Absolute stability criteria that are sufficient for global exponential stability are shown, under a Lipschitz assumption, to be sufficient for the a priori stronger exponential input-to-state stability property. Important corollaries of this result are as follows: (i) absolute stability results obtained using Zames–Falb multipliers for systems containing slope-restricted nonlinearities provide exponential input-to-state-stability under a mild detectability assumption; and (ii) more generally, many absolute stability results obtained via Integral Quadratic Constraint methods provide, with the additional Lipschitz assumption, this stronger property.
Problematic alcohol use and its impact on liver disease quality of life in a multicenter study of patients with cirrhosis
Authors: Luk Jeremy W. , Satre Derek D. , Cheung Ramsey , Wong Robert J. , Monto Alexander , Chen Jennifer Y. , Batki Steven L. , Ostacher Michael J. , Snyder Hannah R. , Shui Amy M. , Liao Meimei , Haight Christina G. , Khalili Mandana 
Published: February 2024

Background:
Management of cirrhosis is challenging and has been complicated by the COVID-19 pandemic due to decreased access to care, increased psychological distress, and alcohol misuse. Recently, The National Institute on Alcohol Abuse and Alcoholism has broadened the definition of recovery from alcohol use disorder to include quality of life (QoL) as an indicator of recovery. This study examined the associations of alcohol-associated cirrhosis etiology and problematic drinking with liver disease QoL (LDQoL).

Methods:
Patients with cirrhosis (N=329) were recruited from 3 sites (63% from 2 Veterans Affairs Health Care Systems and 37% from 1 safety net hospital) serving populations that are economically or socially marginalized. Cirrhosis etiology was ascertained by chart review of medical records. Problematic drinking was defined by ≥8 on the Alcohol Use Disorders Identification Test. Multivariable general linear modeling adjusting for age, sex, race/ethnicity, site, pandemic-related stress, and history of anxiety/depressive disorder were conducted. Sensitivity analyses further adjusted for indicators of liver disease severity.

Results:
Participants were on average 64.6 years old, 17% female, 58% non-White, 44% with alcohol-associated cirrhosis, and 17% with problematic drinking. Problematic drinking was significantly associated with worse LDQoL scores in the overall scale and in the memory/concentration and health distress subscales. These associations remained significant after adjusting for indicators of liver disease severity, including Model for End-Stage Liver Disease-Sodium score and decompensated cirrhosis status.

Conclusions:
Among patients with cirrhosis, problematic drinking was associated with worse LDQoL, especially in the domains of memory/concentration and health distress. Assessment and awareness of cognitive deficits and negative emotionality within the context of cirrhosis and problematic drinking may help clinicians provide better integrated care for this population.
Noninvasive neuromodulation of subregions of the human insula differentially affect pain processing and heart-rate variability: a within-subjects pseudo-randomized trial
Authors: Legon Wynn , Strohman Andrew , In Alexander , Payne Brighton 
Published: February 2024

Abstract
The insula is an intriguing target for pain modulation. Unfortunately, it lies deep to the cortex making spatially specific noninvasive access difficult. Here, we leverage the high spatial resolution and deep penetration depth of low-intensity focused ultrasound (LIFU) to nonsurgically modulate the anterior insula (AI) or posterior insula (PI) in humans for effect on subjective pain ratings, electroencephalographic (EEG) contact heat–evoked potentials, as well as autonomic measures including heart-rate variability (HRV). In a within-subjects, repeated-measures, pseudo-randomized trial design, 23 healthy volunteers received brief noxious heat pain stimuli to the dorsum of their right hand during continuous heart-rate, electrodermal, electrocardiography and EEG recording. Low-intensity focused ultrasound was delivered to the AI (anterior short gyrus), PI (posterior longus gyrus), or under an inert Sham condition. The primary outcome measure was pain rating. Low-intensity focused ultrasound to both AI and PI similarly reduced pain ratings but had differential effects on EEG activity. Low-intensity focused ultrasound to PI affected earlier EEG amplitudes, whereas LIFU to AI affected later EEG amplitudes. Only LIFU to the AI affected HRV as indexed by an increase in SD of N-N intervals and mean HRV low-frequency power. Taken together, LIFU is an effective noninvasive method to individually target subregions of the insula in humans for site-specific effects on brain biomarkers of pain processing and autonomic reactivity that translates to reduced perceived pain to a transient heat stimulus.
Machine learning diagnosis of active Juvenile Idiopathic Arthritis on blood pool [99MTc] Tc-MDP scintigraphy images
Authors: Kian ara Hossein , Alemohammad Nafiseh , Paymani Zeinab , Ebrahimi Marzieh 
Published: February 2024

Purpose
Neural network has widely been applied for medical classifications and disease diagnosis. This study employs deep learning to best discriminate Juvenile Idiopathic Arthritis (JIA), a pediatric chronic joint inflammatory disease, from healthy joints by exploring blood pool images of 2phase [99mTc] Tc-MDP bone scintigraphy.

Methods
Self-deigned multi-input Convolutional Neural Network (CNN) in addition to three available pre-trained models including VGG16, ResNet50 and Xception are applied on 1304 blood pool images of 326 healthy and known JIA children and adolescents (aged 1–16).

Results
The self-designed model ROC analysis shows diagnostic efficiency with Area Under the Curve (AUC) 0.82 and 0.86 for knee and ankle joints, respectively. Among the three pertained models, VGG16 ROC analysis reveals AUC 0.76 and 0.81 for knee and ankle images, respectively.

Conclusion
The self-designed model shows best performance on blood pool scintigraph diagnosis of patients with JIA. VGG16 was the most efficient model rather to other pre-trained networks. This study can pave the way of artificial intelligence (AI) application in nuclear medicine for the diagnosis of pediatric inflammatory disease.
Clinical characteristics and anaesthetic management of severe scoliosis patients with spinal muscular atrophy: case series
Authors: Wang Lai , Du Yi , Huang Na , Yin Na , Du Junming , Yang Junlin , Jiang Lai , Mao Yanfei 
Published: February 2024

Introduction and importance:
There is no expert consensus or guidance on perioperative anaesthesia management for spinal surgery of spinal muscular atrophy (SMA) patients with severe scoliosis (Cobb≧90°). We provide a comprehensive summary of the perioperative characteristics observed in patients with SMA and propose an optimized perioperative management strategy for anaesthesia.

Methods:
This study is a retrospective single-centre research. Twenty-six SMA patients with severe scoliosis underwent posterior spinal fusion surgery from September 2019 to September 2022 were enroled. The main outcomes were to show the patients’ characteristics in anaesthesia, intra- and post-operative periods.

Outcomes:
Nineteen patients underwent awake transnasal/transairway intubation. The median anaesthesia time of 25 patients treated under total intravenous anaesthesia was 425 min. After operation, the Cobb angle and correction rate in the coronal plane were median 54.0° and 54.4%. The length of mechanical ventilation with endotracheal intubation in ICU was median 17.5 h in 8 patients. The ICU length of stay of postoperative hospital was median 19 days. Postoperative pneumonia developed in nine patients, atelectasis in two patients, and pleural effusion in six patients. All patients did not need special oxygen therapy after discharge.

Conclusion:
Multidisciplinary consultation, lung-protective ventilation strategy, appropriate anaesthetic drugs and reasonable blood transfusion scheme and postoperative monitoring were important in anaesthesia, intraoperative and postoperative periods in the patients of severe scoliosis with spinal muscular atrophy.
Prognostic indicators for hospitalization and ICU admission in people with multiple sclerosis and COVID-19: an analysis of the COVID-19 in MS global data sharing initiative dataset
Authors: Garcia-Dominguez Maria A. , Kipkorir Vincent , Srichawla Bahadar S. 
Published: February 2024

Objectives:
To analyze the symptoms and severity of coronavirus disease 2019 (COVID-19) in people with multiple sclerosis (pwMS) on disease-modifying therapies using data from the COVID-19 in multiple sclerosis (MS) Global Data Sharing Initiative dataset.

Methods:
The open-access COVID-19 in MS Global Data Sharing Initiative dataset was obtained through credentialed access using PhysioNet. The variables analyzed included BMI, symptoms of COVID-19, age, current use of disease-modifying therapy (DMT), efficacy of DMT, comorbidities, hospitalization status, and type of MS. A linear regression analysis was completed. Data analysis and visualization were completed using STATA v15, R-Studio v1.1.447, Python v3.8, and its associated libraries, including NumPy, Pandas, and Matplotlib.

Results:
A total of 1141 participants were included in the analysis. 904 women and 237 men were diagnosed with MS. Among the pwMS included in the study; 208 (19.54%) had a suspected infection with COVID-19 and only 49 (5.25%) were confirmed. Any COVID-19 symptom was present in 360 individuals. The commonly reported DMT agents included dimethyl fumarate (12.71%) and fingolimod (10.17%). 101 in total (8.85%) reported not using any DMT. Factors associated with hospitalization and/or admission to the ICU included having any comorbidity (P=0.01), neuromuscular disorder (P=0.046), hypertension (P=0.005), chronic kidney disease (P<0.001), and immunodeficiency (P=0.003). The type of MS, the duration of the disease, and high-efficacy DMT therapy did not have a statistically significant influence on hospitalization.

Conclusion:
This study underscores the importance of comorbidities, especially neuromuscular disorders, hypertension, chronic kidney disease, and immunodeficiencies, as possible prognostic indicators for worse outcomes of COVID-19 in pwMS. On the contrary, the type of MS, the duration of the disease, and the efficacy of disease-modifying therapy did not significantly affect the severity of the symptoms of COVID-19 in this cohort.
Barriers and facilitators of readiness for hospital discharge in patients with myocardial infarction: a qualitative study: quality improvement study
Authors: Monfared Arezoo , Javadi-Pashaki Nazila , Dehghan Nayeri Nahid , Jafaraghaee Fateme 
Published: February 2024

Background:
Readiness for hospital discharge (RHD) in patients with myocardial infarction (MI) is a key concept in the discharge process. This study was conducted to explain the barriers and facilitators of preparation for discharge in patients with MI.

Materials and methods:
A qualitative study was conducted using conventional content analysis from April 2021 to 2022. Data collection was done in a targeted manner through semi-structured interviews until the data saturation stage. Nineteen participants (11 patients, 3 family members, and 5 healthcare team) were interviewed. Data analysis was done in eight steps according to the suggested steps of Graneheim and Lundman. MAXQDA18 software was used for coding.

Results:
From the interviews, 348 primary codes, 11 sub-categories, and 5 categories were extracted. Finally, after continuous analysis and comparison of interviews, codes, and categories, two themes including “supporting platform” and “caring atmosphere” were extracted, which were the result of the participants’ experience and understanding of the barriers and facilitators of RHD. The supporting platform included “family support” and “social support” and the caring atmosphere included “care gaps” and “professional healthcare team performance”.

Conclusion:
The results of this study indicate several factors affecting RHD in heart attack patients. According to the results of the study, the participation of the patient, family, healthcare team, and community in creating RHD is recommended. It is also suggested to pay attention to these factors in care and treatment planning to help improve health and control complications and prevent re-hospitalization in these patients.
Accuracy of Pediatric Interventricular Septal Thickness Measurement Obtained Via Point-of-Care Ultrasound
Authors: Hasan Ahmed , Dahan Nessy , Ayeni Adetunbi , Chhabra Manoj , McGrory Patrick 
Published: February 2024

Background
Hypertrophic cardiomyopathy is a genetic, life-threatening cardiovascular disease that often goes unidentified in pediatric patients. Patients are often asymptomatic and neither history or physical examination are reliable to detect the disease. The only reliable method to diagnose hypertrophic cardiomyopathy is with echocardiography to look at interventricular septal thickness. Emerging literature has shown that cardiac point-of-care ultrasound (POCUS) performed by pediatric emergency medicine (PEM) physicians is as effective and accurate compared with cardiac echocardiography performed by pediatric cardiologists.

Objective
The objective of the study was to determine the diagnostic accuracy of POCUS performed by ultrasound-trained PEM physicians in measuring the interventricular septum end diastole (IVSd) thickness in the pediatric emergency department.

Methods
We conducted a prospective, single-center, observational, diagnostic accuracy study to examine the diagnostic accuracy of POCUS in measuring IVSd thickness in pediatric patients who presented to the pediatric emergency department with symptoms that prompted a cardiac POCUS. Cardiac POCUS findings were interpreted by a PEM physician at the bedside and retrospectively by a pediatric cardiologist. Diagnostic concordance of the measurements obtained by the PEM physician and cardiologist was assessed.

Results
Forty-eight patients were enrolled. Median patient age was 13.4 years. There was excellent diagnostic agreement on the measurement of the IVSd thickness between PEM physicians and the pediatric cardiologist (81.25% of cases; 39/48). Disagreement was seen in 18.75% of the cases (9/48). The mean error of disagreement was −0.32, with a 95% confidence interval of −0.37 to −0.28. Overall, the mean error of both agreement and disagreement was −0.046, with 95% confidence interval of −0.08 to −0.01 and P value of 0.008.

Conclusions
Point-of-care ultrasound performed by ultrasound-trained PEM physicians to measure pediatric IVSd thickness has a high diagnostic accuracy with excellent agreement with a pediatric cardiologist.