Miracle-dz/UniFAR-FTUs huggingface dataset:
A randomized trial to assess the impact of opinion leader endorsed evidence summaries on the use of secondary prevention strategies in patients with coronary artery disease : the ESP-CAD trial protocol [ NCT 00175240 ]

Background: Although numerous therapies have been shown to be beneficial in the prevention of myocardial infarction and/or death in patients with coronary disease, these therapies are underused and this gap contributes to sub-optimal patient outcomes. To increase the uptake of proven efficacious therapies in patients with coronary disease, we designed a multifaceted quality improvement intervention employing patient-specific reminders delivered at the point-of-care, with one-page treatment guidelines endorsed by local opinion leaders ("Local Opinion Leader Statement"). This trial is designed to evaluate the impact of these Local Opinion Leader Statements on the practices of primary care physicians caring for patients with coronary disease. In order to isolate the effects of the messenger (the local opinion leader) from the message, we will also test an identical quality improvement intervention that is not signed by a local opinion leader ("Unsigned Evidence Statement") in this trial. Methods: Randomized trial testing three different interventions in patients with coronary disease: (1) usual care versus (2) Local Opinion Leader Statement versus (3) Unsigned Evidence Statement. Patients diagnosed with coronary artery disease after cardiac catheterization (but without acute coronary syndromes) will be randomly allocated to one of the three interventions by cluster randomization (at the level of their primary care physician), if they are not on optimal statin therapy at baseline. The primary outcome is the proportion of patients demonstrating improvement in their statin management in the first six months post-catheterization. Secondary outcomes include examinations of the use of ACE inhibitors, anti-platelet agents, beta-blockers, non-statin lipid lowering drugs, and provision of smoking cessation advice in the first six months post-catheterization Published: 06 May 2006 Implementation Science 2006, 1:11 doi:10.1186/1748-5908-1-11 Received: 08 March 2006 Accepted: 06 May 2006 This article is available from: http://www.implementationscience. com/content/1/1/11 © 2006 McAlister et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Identification and Validation of a Prognostic Signature Derived from the Cancer Stem Cells for Oral Squamous Cell Carcinoma

The progression and metastasis of oral squamous cell carcinoma (OSCC) are highly influenced by cancer stem cells (CSCs) due to their unique self-renewal and plasticity. In this study, data were obtained from a single-cell RNA-sequencing dataset (GSE172577) in the GEO database, and LASSO-Cox regression analysis was performed on 1344 CSCs-related genes to establish a six-gene prognostic signature (6-GPS) consisting of ADM, POLR1D, PTGR1, RPL35A, PGK1, and P4HA1. High-risk scores were significantly associated with unfavorable survival outcomes, and these features were thoroughly validated in the ICGC. The results of nomograms, calibration plots, and ROC curves confirmed the good prognostic accuracy of 6-GPS for OSCC. Additionally, the knockdown of ADM or POLR1D genes may significantly inhibit the proliferation, migration, and invasion of OSCC cells through the JAK/HIF-1 pathway. Furthermore, cell-cycle arrest occurred in the G1 phase by suppressing Cyclin D1. In summary, 6-GPS may play a crucial role in the occurrence and development of OSCC and has the potential to be developed further as a diagnostic, therapeutic, and prognostic tool for OSCC.
General and Anxiety-Linked Influence of Acute Serotonin Reuptake Inhibition on Neural Responses Associated with Attended Visceral Sensation

Serotonin is known to have state-dependent modulatory influences on exteroceptive sensory processes and the processing of pain, but much less is known about its role in ordinary interoceptive processes and their relationships to affective states. This experiment compared the impact of a selective serotonin reuptake inhibitor (SSRI) (20mg CITALOPRAM), acutely increasing extracellular serotonin, to that of a PLACEBO on the neural processing of ordinary interoceptive sensations and the relationship of these influences to anxious states. Twenty-one healthy young volunteers completed the visceral interoceptive attention (VIA) task with each treatment, focusing attention on heart, stomach, or visual sensation control while scanned with functional magnetic resonance imaging (fMRI). The relative neural interoceptive response (IR) to heart sensation [heart minus visual] and stomach sensation [stomach minus visual] were compared between treatment conditions, controlling for general effects on sensory processing. CITALOPRAM reduced interoceptive processing in viscerosensory (bilateral posterior insular cortex, stomach-IR) and integrative/affective components (bilateral amygdala, stomach-IR and heart-IR) of known interoceptive pathways. We then searched for state-dependent modulatory effects of CITALOPRAM that varied with current levels of anxiety. The anterior insular cortex response to heart sensation increased with anxiety, replicating prior findings. This relationship disappeared on CITALOPRAM. Preliminary post hoc exploration found that CITALOPRAM'S effects on amygdalae response to stomach sensation predicted acute increases and decreases in anxiety. Overall, this evidence of general and state-dependent serotonergic influence advances our understanding of interoception, its regulation, and its relationship to anxious states.
Dual-domain joint learning reconstruction method (JLRM) combined with physical process for spectral computed tomography

Spectral computed tomography (SCT) is an powerful imaging modality with broad applications and advantages such as contrast enhancement, artifact reduction, and material differentiation. The positive process or data collected process of SCT is a nonlinear physical process existing scatter and noise, which make it is an extremely ill-posed inverse problem in mathematics. In this paper, we propose a dual-domain iterative network combining a joint learning reconstruction method (JLRM) with a physical process. Specifically, a physical module network is constructed according to the SCT physical process to accurately describe this forward process, which makes the nonlinear use of the traditional mathematical iterative algorithm effective and stable. Additionally, we build a residual-to-residual strategy with an attention mechanism to overcome the slow speed of the traditional mathematical iterative algorithm. We have verified the feasibility of the method through our winning submission to the AAPM DL-spectral CT challenge, and demonstrated that high-accuracy also basis material decomposition results can be achieved with noisy data.
Implementation of the Short-Form 36 (SF-36) in childhood cancer survivors: An analysis on measurement properties across 5 European countries

ABSTRACT Purpose: The Short Form-36 (SF-36) is widely used in many research contexts and cultures to assess health-related quality of life (HRQOL). We investigated the measurement properties of the SF-36 in a large cohort study among childhood cancer survivors living in 5 European countries. Methods: The PanCareLIFE project includes adult survivors of childhood cancer living in the Czech Republic, France, Germany, The Netherlands, and Switzerland. We invited 19,268 survivors aged >18 years, and 10,077 (53%) returned questionnaires. Of these, 9,871 (98%) were included in the analyses. We assessed HRQOL with the SF-36 version 1 (V1) or version 2 (V2) and investigated its performance. We analysed data completeness, floor and ceiling effects, item-internal consistency, item-discriminant validity, reliability, and scaling assumptions focusing on country- and version-specific differences. Results: Data completeness was high but differed between countries (90% in the Czech Republic to 96% in France). Floor effects were negligible, but ceiling effects in 5 out of 8 SF-36 scales were present (50-70%). V2 had slightly fewer ceiling effects than V1 in 2 scales. Item-internal consistency, item-discriminant validity, and reliability were good. Scaling assumptions were met, with 4 scales representing strong mental health content, and the other 4 representing strong physical health content. Conclusion: The SF-36 (V1 and V2) provided robust measurement properties among childhood cancer survivors, and differences in study design across the 5 European countries did not affect measurement properties. Researchers need to take into account that ceiling effects exist which might limit sensitivity among participants who fared particularly well.
CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process

Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain. Traditional CDR studies follow the embedding and mapping (EMCDR) paradigm, which transfers user representations from the source to target domain by learning a user-shared mapping function, neglecting the user-specific preference. Recent CDR studies attempt to learn user-specific mapping functions in meta-learning paradigm, which regards each user's CDR as an individual task, but neglects the preference correlations among users, limiting the beneficial information for user representations. Moreover, both of the paradigms neglect the explicit user-item interactions from both domains during the mapping process. To address the above issues, this paper proposes a novel CDR framework with neural process (NP), termed as CDRNP. Particularly, it develops the meta-learning paradigm to leverage user-specific preference, and further introduces a stochastic process by NP to capture the preference correlations among the overlapping and cold-start users, thus generating more powerful mapping functions by mapping the user-specific preference and common preference correlations to a predictive probability distribution. In addition, we also introduce a preference remainer to enhance the common preference from the overlapping users, and finally devises an adaptive conditional decoder with preference modulation to make prediction for cold-start users with items in the target domain. Experimental results demonstrate that CDRNP outperforms previous SOTA methods in three real-world CDR scenarios.
Shift-ConvNets: Small Convolutional Kernel with Large Kernel Effects

Recent studies reveal that the remarkable performance of Vision transformers (ViTs) benefits from large receptive fields. For this reason, the large convolutional kernel design becomes an ideal solution to make Convolutional Neural Networks (CNNs) great again. However, the typical large convolutional kernels turn out to be hardware-unfriendly operators, resulting in discount compatibility of various hardware platforms. Thus, it is unwise to simply enlarge the convolutional kernel size. In this paper, we reveal that small convolutional kernels and convolution operations can achieve the closing effects of large kernel sizes. Then, we propose a shift-wise operator that ensures the CNNs capture long-range dependencies with the help of the sparse mechanism, while remaining hardware-friendly. Experimental results show that our shift-wise operator significantly improves the accuracy of a regular CNN while markedly reducing computational requirements. On the ImageNet-1k, our shift-wise enhanced CNN model outperforms the state-of-the-art models. Code&models at https://github.com/lidc54/shift-wiseConv.
Bayesian Semi-structured Subspace Inference

Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. The structured model part is inspired by statistical models and can be used to infer the input-output relationship for features of particular importance. The complex unstructured part defines an arbitrary deep neural network and thereby provides enough flexibility to achieve competitive prediction performance. While these models can also account for aleatoric uncertainty, there is still a lack of work on accounting for epistemic uncertainty. In this paper, we address this problem by presenting a Bayesian approximation for semi-structured regression models using subspace inference. To this end, we extend subspace inference for joint posterior sampling from a full parameter space for structured effects and a subspace for unstructured effects. Apart from this hybrid sampling scheme, our method allows for tunable complexity of the subspace and can capture multiple minima in the loss landscape. Numerical experiments validate our approach's efficacy in recovering structured effect parameter posteriors in semi-structured models and approaching the full-space posterior distribution of MCMC for increasing subspace dimension. Further, our approach exhibits competitive predictive performance across simulated and real-world datasets.
Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?

Incorporating human context into language models is the next frontier for human-centered natural language processing. Currently, two pre-training methods exist: group-wise attributes (e.g., over-45-year-olds) or individual traits. Group attributes are coarse -- not all 45-year-olds write the same way -- while modeling individual traits allows for a more personalized representation, but requires more complex modeling and data. So far, it is unclear which pre-training approach benefits what tasks. We compare pre-training models with human context via 1) group attributes, 2) individual users, and 3) a combined approach on 5 user- and document-level tasks. We find that pre-training with both group and individual features significantly improves the two user-level regression tasks like age estimation and personality assessment. Pre-training on individual users significantly improves the three document-level classification tasks like stance and topic detection. It even does well for downstream tasks without historical user data. Our results suggest both approaches have specific use cases, opening new avenues for human-centered language modeling.
Integrating Individual Process and Population Pattern across Spatial Scales: Habitat Selection, Distribution, and Demography of a Northern Ungulate, Alces alces

INTEGRATING INDIVIDUAL PROCESS AND POPULATION PATTERN ACROSS SPATIAL SCALES: HABITAT SELECTION, DISTRIBUTION, AND DEMOGRAPHY OF A NORTHERN UNGULATE, ALCES ALCES Garrett M. Street Advisor: University of Guelph, 2014 John M. Fryxell Evaluating how animals behave and how populations perform across broad bioclimatic gradients is critical to a complete understanding of a species’ ecology. Given a landscape of varying habitat types and quality, individuals should select habitats that provide the greatest benefits to individual fitness, ultimately distributing themselves such that localized densities at equilibrium should positively covary with increasing habitat quality. Variation in habitat selection should give rise to demographic patterns across space, and knowledge of how one influences the other should provide new insight into how and why behaviour and demography may change over space. In my PhD thesis, I develop individually-based models of habitat selection, and population-based models of demography as driven by landscape configuration, pursuant to exploring the interrelatedness of individual preference and population distribution. In the first chapter, I estimate a model of habitat selection of moose (Alces alces) as a function of temperature variation across seasons based on fine-scale global positioning data in northwestern Ontario. In the second chapter, I estimate moose habitat selection models using recently updated landscape layers and aerial census data (i.e., presence/absence) across two spatially distinct study sites. In the third chapter, I estimate carrying capacities and intrinsic growth rates of moose populations using time series of abundance and hunting mortality across the managed forests of Ontario. In the third chapter, I estimate carrying capacities and intrinsic growth rates of moose populations using time series of abundance and hunting mortality across the managed forests of Ontario. In the fourth chapter, I use the models of habitat selection and carrying capacity to predict moose density in a third, novel site, as well as compare density predictions from the habitat model to carrying capacities derived in the third chapter. Ultimately, my thesis provides an explicit evaluation of population prediction based on both habitat selection and demographic patterns, and illustrates the dependency of individual processes (i.e., habitat selection) and population pattern (i.e., demography) on similar patterns of landscape quality and configuration.
Existence of three distinct scaling regimes in self-propelled rigid pitching airfoil

Oscillating foils in self-propelled mode are the simplest model for investigating oscillatory locomotion in cruising fishes. In this investigation, we explore the self-propulsion characterisitics of a NACA0015 section airfoil, with chord length $C$, subjected to sinusoidal pitching using a rotary apparatus. A power-spring-based crank-rocker mechanism actuates the airfoil. We examine the effect of pitching frequency ($f$), amplitude ($A$), and the pitching point location ($p$) on the self-propulsion speed, $U_s$. We present the results in terms of self-propulsion Reynolds number ($Re_s = U_sC/\nu$), reduced frequency ($k_s$), Strouhal number ($St$), and two non-dimensional speeds, $U^*_{BL}$ (body length per oscillation) and $U^*_{AL}$ (forward speed in terms of trailing edge excursion per oscillation). $U_s$ increases with frequency and amplitude, but a diminishing effect of amplitude was noted at larger amplitudes. Highest speeds were achieved when pitched closest to the leading edge, with $St$ in the range of $0.2 - 0.4$. Three distinct scaling regimes are identified, each characterized by a specific relationship between $Re_s$ and trailing-edge Reynolds number, $Re_{TE} = fAC/\nu$. When pitching at low-amplitude, close to leading-edge, $Re_s \sim (1-2p) Re_{TE}^{3/2}$ (power scaling). For higher amplitude pitching, $Re_s \sim Re_{TE} (1-2p)^{1/2} (A/C)^{-1/2}$ (separable scaling). When pitched close to the midpoint, the airfoil propels beyond a threshold $Re_{TE,0}$, and $Re_s$ increases linearly with $Re_{TE}$ (linear scaling). Notably, in separable and linear regimes, self-propulsion is independent of viscosity. These relations collectively offer a comprehensive framework for understanding the self-propulsion of rigid pitching airfoils across a wide range of parameters.
Spectral Smoothness of Ground Plane Backed Log-Periodic Dipole Antennas for Radioastronomical Applications

The spectral smoothness properties of the low-frequency array of the square kilometer array (SKA), namely, SKA-Low, are an important issue for its scientific objectives to be attainable. A large array of 256 log-periodic dipole antennas, installed on top of a 42 m circular ground plane, will work as an SKA-Low station in the frequency range of 50–350 MHz. In this article, the ground plane-induced effects are examined in terms of antenna beam spectral characteristics, while different antenna placements are considered. Results are produced both at the isolated antenna and at the array level in the band 50–100 MHz, by employing an approximate method for the speeding-up of array simulations. We attempt to distinguish the ground plane effect from that of mutual coupling among antennas, which appears to be more severe at specific frequencies, using 2 figures of merit. The discrete Fourier transform (DFT) components of gain pattern ratios identify the fundamental spatial components of the ripple, while the envelope correlation coefficient quantifies the penalty to considering an infinite ground plane.
Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning

Reinforcement learning is an emerging approaches to facilitate multi-stage sequential decision-making problems. This paper studies a real-time multi-stage stochastic power dispatch considering multivariate uncertainties. Current researches suffer from low generalization and practicality, that is, the learned dispatch policy can only handle a specific dispatch scenario, its performance degrades significantly if actual samples and training samples are inconsistent. To fill these gaps, a novel contextual meta graph reinforcement learning (Meta-GRL) for a highly generalized multi-stage optimal dispatch policy is proposed. Specifically, a more general contextual Markov decision process (MDP) and scalable graph representation are introduced to achieve a more generalized multi-stage stochastic power dispatch modeling. An upper meta-learner is proposed to encode context for different dispatch scenarios and learn how to achieve dispatch task identification while the lower policy learner learns context-specified dispatch policy. After sufficient offline learning, this approach can rapidly adapt to unseen and undefined scenarios with only a few updations of the hypothesis judgments generated by the meta-learner. Numerical comparisons with state-of-the-art policies and traditional reinforcement learning verify the optimality, efficiency, adaptability, and scalability of the proposed Meta-GRL.
Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems

In Integrated Sensing and Communication (ISAC) systems, matching the radar targets with communication user equipments (UEs) is functional to several communication tasks, such as proactive handover and beam prediction. In this paper, we consider a radar-assisted communication system where a base station (BS) is equipped with a multiple-input-multiple-output (MIMO) radar that has a double aim: (i) associate vehicular radar targets to vehicular equipments (VEs) in the communication beamspace and (ii) predict the beamforming vector for each VE from radar data. The proposed target-to-user (T2U) association consists of two stages. First, vehicular radar targets are detected from range-angle images, and, for each, a beamforming vector is estimated. Then, the inferred per-target beamforming vectors are matched with the ones utilized at the BS for communication to perform target-to-user (T2U) association. Joint multi-target detection and beam inference is obtained by modifying the you only look once (YOLO) model, which is trained over simulated range-angle radar images. Simulation results over different urban vehicular mobility scenarios show that the proposed T2U method provides a probability of correct association that increases with the size of the BS antenna array, highlighting the respective increase of the separability of the VEs in the beamspace. Moreover, we show that the modified YOLO architecture can effectively perform both beam prediction and radar target detection, with similar performance in mean average precision on the latter over different antenna array sizes.