Miracle-dz/UniFAR-FTUs huggingface dataset:
Endomysial fibrosis, rather than overall connective tissue content, is the main determinant of conduction disturbances in human atrial fibrillation

Abstract Aims Although in persistent atrial fibrillation (AF) a complex AF substrate characterized by a high incidence of conduction block has been reported, relatively little is known about AF complexity in paroxysmal AF (pAF). Also, the relative contribution of various aspects of structural alterations to conduction disturbances is not clear. In particular, the contribution of endomysial fibrosis to conduction disturbances during progression of AF has not been studied yet. Methods and results During cardiac surgery, epicardial high-density mapping was performed in patients with acutely induced (aAF, n = 11), pAF (n = 12), and longstanding persistent AF (persAF, n = 9) on the right atrial (RA) wall, the posterior left atrial wall (pLA) and the LA appendage (LAA). In RA appendages, overall and endomysial (myocyte-to-myocyte distances) fibrosis and connexin 43 (Cx43) distribution were quantified. Unipolar AF electrogram analysis showed a more complex pattern with a larger number of narrower waves, more breakthroughs and a higher fractionation index (FI) in persAF compared with aAF and pAF, with no differences between aAF and pAF. The FI was consistently higher at the pLA compared with the RA. Structurally, Cx43 lateralization increased with AF progression (aAF = 7.5 ± 8.9%, pAF = 24.7 ± 11.1%, persAF = 35.1 ± 11.4%, P < 0.001). Endomysial but not overall fibrosis correlated with AF complexity (r = 0.57, P = 0.001; r = 0.23, P = 0.20; respectively). Conclusions Atrial fibrillation complexity is highly variable in patients with pAF, but not significantly higher than in patients with acutely induced AF, while in patients with persistent AF complexity is higher. Among the structural alterations studied, endomysial fibrosis, but not overall fibrosis, is the strongest determinant of AF complexity.
Multimedia Data-Based Mobile Applications for Dietary Assessment

Diabetes mellitus (DM) and obesity are chronic medical conditions associated with significant morbidity and mortality. Accurate macronutrient and energy estimation could be beneficial in attempts to manage DM and obesity, leading to improved glycemic control and weight reduction, respectively. Existing dietary assessment methods are subject to major errors in measurement, are time consuming, are costly, and do not provide real-time feedback. The increasing adoption of smartphones and artificial intelligence, along with the advances in algorithms and hardware, allowed the development of technologies executed in smartphones that use food/beverage multimedia data as an input, and output information about the nutrient content in almost real time. Scope of this review was to explore the various image-based and video-based systems designed for dietary assessment. We identified 22 different systems and divided these into three categories on the basis of their setting for evaluation: laboratory (12), preclinical (7), and clinical (3). The major findings of the review are that there is still a number of open research questions and technical challenges to be addressed and end users—including health care professionals and patients—need to be involved in the design and development of such innovative solutions. Last, there is a clear need that these systems should be validated under unconstrained real-life conditions and that they should be compared with conventional methods for dietary assessment.
Racial/ethnic disparities in infertility treatment utilization in the US, 2011–2019

Abstract With delayed child-bearing age, there has been an increase in infertility rates globally and in the United States (US). Unsurprisingly, there has been a concomitant substantial increase in the number of individuals seeking infertility treatments over the last decade. This study aimed to examine the relationship between race/ethnicity and the utilization of different infertility treatments over the previous decade. We conducted this retrospective cohort study using the United States (US) Birth data files 2011–2019. We calculated the rates of infertility treatment and its subtypes over the study period. Descriptive statistics were utilized to examine the sociodemographic and birth characteristics for overall births and those associated with any infertility treatment and each of its subtypes. We calculated the level of association between race/ethnicity and utilization of infertility treatment and the subtypes using adjusted logistic regression models. We found that the rate of infertility treatments for all subtypes considered, had steadily increased by 63.7% within the past decade. In contrast, fertility enhancing drugs or Intrauterine Insemination (IUI) increased by 134%, and in vitro fertilization (IVF), gamete intrafallopian transfer (GIFT), and zygote intrafallopian transfer (ZIFT) treatments increased by 40% over the 9-year study period. Non-Hispanic (NH) Asian women had the highest rate of any infertility treatment with a rate of 25 per 1000 births whereas Hispanic women had the lowest rate of any infertility treatment at 5.8 per 1000 births. When compared with NH-White women, NH-Asian women had a modest 7% lower likelihood (OR = 0.93, 95% CI = 0.92–0.94) of receiving any infertility treatment while NH-Black and Hispanic women had about 70% lower likelihood of receiving any infertility treatment. Our report of increased assisted reproductive technology (ART) utilization rates, and marked racial/ethnic differences in ART utilization highlight the importance of expanding knowledge of inequities that continue to impact marginalized groups, a critical step for informing actionable strategy formulations (i.e., advocacy, policy change, patient education, provider training) to address these inequities.
Outcomes of 25 female dogs treated for ectopic ureters by open surgery or cystoscopic-guided laser ablation.

OBJECTIVE To report outcomes after the correction of ectopic ureter (EU) by open surgery or cystoscopic-guided laser ablation (CLA) in female dogs. STUDY DESIGN Retrospective study from 2011 to 2018. ANIMALS Twenty-five female dogs. METHODS Data collected included signalment, clinicopathologic data, procedural data, complications, and short-term and long-term outcomes. Complications were graded as minor or major if a surgical revision was required. Continence status was scored subjectively (1 = completely incontinent to 10 = fully continent). RESULTS Fifteen dogs had bilateral EU and 24 had intramural EU (iEU). Open surgical correction included 13 neoureterostomies, 2 neocystoureterostomies, and a combination of these in 2 dogs. Eight dogs underwent CLA. Eighteen dogs experienced minor complications (72%), and 2 experienced major complications (8%). One-month postoperative continence was achieved in 20/25 (80%) dogs (median score of 10). Incontinence recurred at a median time of 24.9 months in 5 dogs but responded to medical treatment. Overall, dogs remained continent for 66 months (median) and 22/25 (88%) dogs achieved continence with adjunction of medical/surgical treatment in incontinent ones. Fewer minor complications and postoperative recurrences of incontinence were documented after CLA than neoureterostomy (P < .01 and P < .05). CONCLUSION Ectopic ureter correction by open surgery or CLA resulted in a subjectively good prognosis, most dogs reaching continence within a month of surgery, although incontinence occasionally recurred in the long term. CLA was associated with fewer complications and incontinence recurrences than neoureterostomy. CLINICAL SIGNIFICANCE Cystoscopic-guided laser ablation should be preferred to correct iEU to prevent short-term complications and the recurrence of incontinence. Further studies should investigate the cause of postoperative recurrence of urinary incontinence.
Effects of Pulsatility on Arterial Endothelial and Smooth Muscle Cells

Continuous flow ventricular assist device (CFVAD) support in advanced heart failure patients causes diminished pulsatility, which has been associated with adverse events including gastrointestinal bleeding, end organ failure, and arteriovenous malformation. Recently, pulsatility augmentation by pump speed modulation has been proposed as a means to minimize adverse events. Pulsatility primarily affects endothelial and smooth muscle cells in the vasculature. To study the effects of pulsatility and pulse modulation using CFVADs, we have developed a microfluidic co-culture model with human aortic endothelial (ECs) and smooth muscle cells (SMCs) that can replicate physiologic pressures, flows, shear stresses, and cyclical stretch. The effects of pulsatility and pulse frequency on ECs and SMCs were evaluated during (1) normal pulsatile flow (120/80 mmHg, 60 bpm), (2) diminished pulsatility (98/92 mmHg, 60 bpm), and (3) low cyclical frequency (115/80 mmHg, 30 bpm). Shear stresses were estimated using computational fluid dynamics (CFD) simulations. While average shear stresses (4.2 dynes/cm2) and flows (10.1 mL/min) were similar, the peak shear stresses for normal pulsatile flow (16.9 dynes/cm2) and low cyclic frequency (19.5 dynes/cm2) were higher compared to diminished pulsatility (6.45 dynes/cm2). ECs and SMCs demonstrated significantly lower cell size with diminished pulsatility compared to normal pulsatile flow. Low cyclical frequency resulted in normalization of EC cell size but not SMCs. SMCs size was higher with low frequency condition compared to diminished pulsatility but did not normalize to normal pulsatility condition. These results may suggest that pressure amplitude augmentation may have a greater effect in normalizing ECs, while both pressure amplitude and frequency may be required to normalize SMCs morphology. The co-culture model may be an ideal platform to study flow modulation strategies.
Static Magnetic Field Promotes Proliferation, Migration, Differentiation, and AKT Activation of Periodontal Ligament Stem Cells

Periodontal ligament stem cells (PDLSCs) possess self-renewal and multilineage differentiation potential and exhibit great potential for the treatment of bone tissue defects caused by inflammation. Previous studies have indicated that static magnetic field (SMF) can enhance the proliferation and differentiation of mesenchymal stem cells (MSCs). SMF has been widely used to repair bone defects and for orthodontic and implantation treatment. In this study, we revealed that a 320 mT SMF upregulates the protein expression levels of cytokines such as MCM7 and PCNA in proliferating PDLSCs. Cell counting kit-8 results revealed that the SMF group had higher optical density values than the control group. The ratio of cells in the S phase to those in the G2/M phase was significantly increased after exposure to a 320 mT SMF. In scratch assays, the SMF-treated PDLSCs exhibited a higher migration rate than the sham-exposed group after 24 h of culture, indicating that the SMF promoted the migratory ability of PDLSCs. The activity level of the early differentiation marker alkaline phosphatase and the late marker matrix mineralization, as well as osteoblast-specific gene and protein expression, were enhanced in PDLSCs exposed to the SMF. Furthermore, AKT signaling pathway was activated by SMF. Our data demonstrated that the potential mechanism of action of SMF may enhance PDLSCs proliferation and osteogenic differentiation by activating the phosphorylated AKT pathway. The elucidation of this molecular mechanism may lead to a better understanding of bone repair responses and aid in improved stem cell-mediated regeneration.
Recognition of facial emotion expressions and perceptual processes in 22q11.2 deletion syndrome

Social cognition (SC) deficits and of its facial emotion expression (FEE) component have been described in 22q11.2 Deletion Syndrome (22q11.2DS), a high‐risk for schizophrenia (SCZ) systemic genetic syndrome. Correlations between deficits in FEE skills and visual‐spatial abilities in people with 22q11.2DS warrant investigation.
A Single Dose of Ibuprofen Impacts IL-10 Response to 164-km Road Cycling in the Heat

ABSTRACT Purpose: The purpose was to determine the effect of a single-dose prophylactic ibuprofen use before a 164-km road cycling event in high ambient temperature on the circulating cytokine and leukocyte responses. Methods: Twenty-three men (53 ± 8 y, 172.0 ± 22.0 cm, 85.1 ± 12.8 kg, 19.6 ± 4.4% body fat) completed a 164-km self-paced recreational road cycling event in a hot, humid, sunny environment (WBGT = 29.0 ± 2.9°C) after consuming 600 mg of ibuprofen (n = 13) or a placebo (n = 10). Blood samples were obtained one to two hours before (PRE) and immediately after (POST) the event, and analyzed for concentrations of circulating cytokines interleukins (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL-13, GM-CSF, IFN-γ, and TNF-α and leukocytes (total leukocytes, granulocytes, monocytes, and lymphocytes). Results: Event completion time was 400.2 ± 74.8 min. Concentrations of all cytokines (except IL-1β, IL-2, IL-5, IL-12, GM-CSF, and IFN-γ) and of all leukocyte subsets increased from PRE to POST. Ibuprofen ingestion attenuated the increase in IL-10 (86% increase with Ibuprofen; 270% increase with placebo). Conclusions: Consuming 600 mg of Ibuprofen prior to a 164-km road cycling event in a hot-humid environment attenuates exercise-induced increases in the concentration of the anti-inflammatory cytokine IL-10, but does not alter the effect of the exercise event on concentrations of other circulating cytokines or leukocyte subset concentrations.
Original Research: Losing the Art and Failing the Science of Nursing: The Experiences of Nurses Working During the COVID-19 Pandemic

ABSTRACT Purpose: RNs have served as the bedrock of the response to the COVID-19 pandemic, working under unprecedented and difficult conditions. In this study, we sought to understand the experiences of nurses working across a range of care settings in the United States during the first six months of the pandemic, and to learn more about barriers to and facilitators of their work. Methods: This is a qualitative descriptive study. We recruited participants online through regional professional nursing membership listservs, program directors of occupational health nursing training programs, and social media. After completing a survey, potential participants were invited to complete an individual semistructured interview via the Zoom platform. From June through August 2020, we conducted 34 interviews. Content analysis was performed using ATLAS.ti software. Results: The overarching theme—“Losing the art and failing the science of nursing”—underscored the barriers nurses faced in the early months of this pandemic. It reflected the deeply painful disruptions in the care nurses were accustomed to providing their patients. Themes that reflected barriers included disrupted nurse–patient connection, lack of personal protective equipment and fear of infection, lack of evidence-based guidance, and understaffing, all of which drastically altered the delivery of nursing care. Themes that reflected facilitators to nurses' work included camaraderie and strength and resourcefulness. Conclusions: The study findings give important direction to nurse leaders, researchers, and organizations concerning potential areas of support that nurses need during and after this pandemic. Future research should investigate the long-term impact of COVID-19 and similar public health crises on nurses, as well as interventions that could support the workforce after an extended crisis. The authors of this qualitative descriptive study sought to understand nurses' work experiences in various U.S. health care settings during the first six months of the COVID-19 pandemic, and to learn more about barriers to and facilitators of their work.
Neural connectivity underlying adolescent social learning in sibling dyads

Abstract Social learning theory posits that adolescents learn to adopt social norms by observing the behaviors of others and internalizing the associated outcomes. However, the underlying neural processes by which social learning occurs is less well-understood, despite extensive neurobiological reorganization and a peak in social influence sensitivity during adolescence. Forty-four adolescents (Mage = 12.2 years) completed an fMRI scan while observing their older sibling within four years of age (Mage = 14.3 years) of age complete a risky decision-making task. Group iterative multiple model estimation (GIMME) was used to examine patterns of directional brain region connectivity supporting social learning. We identified group-level neural pathways underlying social observation including the anterior insula to the anterior cingulate cortex and mentalizing regions to social cognition regions. We also found neural states based on adolescent sensitivity to social learning via age, gender, modeling, differentiation, and behavior. Adolescents who were more likely to be influenced elicited neurological up-regulation whereas adolescents who were less likely to be socially influenced elicited neurological down-regulation during risk-taking. These findings highlight patterns of how adolescents process information while a salient influencer takes risks, as well as salient neural pathways that are dependent on similarity factors associated with social learning theory.
Convergence and Complexity of Stochastic Subgradient Methods with Dependent Data for Nonconvex Optimization

A BSTRACT . We show that under a general dependent data sampling scheme, the classical stochastic projected and proximal subgradient methods for weakly convex functions have worst-case rate of convergence ˜ O ( n − 1/4 ) and complexity ˜ O ( ε − 4 ) for achieving an ε -near stationary point in terms of the norm of the gradient of Moreau envelope. While classical convergence guarantee requires i
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection

In this paper, we propose the LiDAR Distillation to bridge the domain gap induced by different LiDAR beams for 3D object detection. In many real-world applications, the LiDAR points used by mass-produced robots and vehicles usually have fewer beams than that in large-scale public datasets. Moreover, as the LiDARs are upgraded to other product models with different beam amount, it becomes challenging to utilize the labeled data captured by previous versions' high-resolution sensors. Despite the recent progress on domain adaptive 3D detection, most methods struggle to eliminate the beam-induced domain gap. We find that it is essential to align the point cloud density of the source domain with that of the target domain during the training process. Inspired by this discovery, we propose a progressive framework to mitigate the beam-induced domain shift. In each iteration, we first generate low-beam pseudo LiDAR by downsampling the high-beam point clouds. Then the teacher-student framework is employed to distill rich information from the data with more beams. Extensive experiments on Waymo, nuScenes and KITTI datasets with three different LiDAR-based detectors demonstrate the effectiveness of our LiDAR Distillation. Notably, our approach does not increase any additional computation cost for inference.
Pareto Set Learning for Neural Multi-objective Combinatorial Optimization

Multiobjective combinatorial optimization (MOCO) problems can be found in many real-world applications. However, exactly solving these problems would be very challenging, particularly when they are NP-hard. Many handcrafted heuristic methods have been proposed to tackle different MOCO problems over the past decades. In this work, we generalize the idea of neural combinatorial optimization, and develop a learning-based approach to approximate the whole Pareto set for a given MOCO problem without further search procedure. We propose a single preference-conditioned model to directly generate approximate Pareto solutions for any trade-off preference, and design an efficient multiobjective reinforcement learning algorithm to train this model. Our proposed method can be treated as a learning-based extension for the widely-used decomposition-based multiobjective evolutionary algorithm (MOEA/D). It uses a single model to accommodate all the possible preferences, whereas other methods use a finite number of solutions to approximate the Pareto set. Experimental results show that our proposed method significantly outperforms some other methods on the multiobjective traveling salesman problem, multiobjective vehicle routing problem, and multiobjective knapsack problem in terms of solution quality, speed, and model efficiency.
Resonance, symmetry, and bifurcation of periodic orbits in perturbed Rayleigh–Bénard convection

This paper investigates the global structures of periodic orbits that appear in Rayleigh–Bénard convection, which is modelled by a two-dimensional perturbed Hamiltonian model, by focusing upon resonance, symmetry and bifurcation of the periodic orbits. First, we show the global structures of periodic orbits in the extended phase space by numerically detecting the associated periodic points on the Poincaré section. Then, we illustrate how resonant periodic orbits appear and specifically clarify that there exist some symmetric properties of such resonant periodic orbits which are projected on the phase space; namely, the period m and the winding number n become odd when an m-periodic orbit is symmetric with respect to the horizontal and vertical centre lines of a cell. Furthermore, the global structures of bifurcations of periodic orbits are depicted when the amplitude ɛ of the perturbation is varied, since in experiments the amplitude of the oscillation of the convection gradually increases when the Rayleigh number is raised.
Sacrificing CSI for a Greater Good: RIS-enabled Opportunistic Rate Splitting

In reconfigurable intelligent surface (RIS)-assisted systems, the optimization of the phase shifts requires separate acquisition of the channel state information (CSI) for the direct and RIS-assisted channels, posing significant design challenges. In this paper, a novel scheme is proposed, which considers practical limitations like pilot overhead and channel estimation (CE) errors to increase the net performance. More specifically, at the cost of unpredictable interference, a portion of the CSI for the RIS-assisted channels is sacrificed in order to reduce the CE time. By alternating the CSI between coherence blocks and employing rate splitting, it becomes possible to mitigate the interference, thereby compensating the adverse effect of the sacrificed CSI. Numerical simulations validate that the proposed scheme exhibits better performance in terms of achievable net rate, resulting in gains of up to 160% compared non-orthogonal multiple access (NOMA), when CE time and CE errors are considered.
Stochastic Parameterizations: Better Modelling of Temporal Correlations using Probabilistic Machine Learning

The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Using stochasticity and machine learning have led to better models but there is a lack of work on combining the beneﬁts from both. We show that by using a physically-informed recurrent neural network within a probabilistic framework, our resulting model for the Lorenz 96 atmospheric simulation is competitive and often superior to both a bespoke baseline and an existing probabilistic machine-learning (GAN) one. This is due to a superior ability to model temporal correlations compared to standard ﬁrst-order autoregressive schemes. The model also generalises to unseen regimes. We evaluate across a number of metrics from the literature, but also discuss how the probabilistic metric of likelihood may be a unifying choice for future probabilistic climate models.