Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of political ideology on awareness of HPV and HPV vaccine among adults in the United States

ABSTRACT This study aimed to investigate the association between political ideology and awareness of HPV and HPV vaccine among US adults. Study data were derived from Health Information National Trends Survey 5 Cycle 4, a 2020 cross-sectional survey of US adults. Multivariable logistic regressions were conducted to examine the association between political ideology with HPV and HPV vaccine awareness. A total of 3418 adults participated in the study, with the majority being non-Hispanic White individuals and women. The results showed that 66.1% and 62.3% of the participants were aware of HPV and HPV vaccine, respectively. A total of 36.9%, 29.7% and 33.4% of the population reported moderate, liberal and conservative political ideologies respectively. Awareness levels were highest among liberals, with 77.1% and 72.7% reporting awareness of HPV and HPV vaccine respectively. Multivariable logistic regression analysis revealed that liberal participants were more likely to be aware of HPV (aOR, 1.94; 95% CI, 1.25–3.00), and HPV vaccines (aOR, 1.94; 95% CI, 1.37–2.74) compared to moderates. Also, liberals had higher odds of HPV (aOR, 2.41; 95% CI, 1.65–3.51), and HPV vaccine awareness (aOR, 1.91; 95% CI, 1.29–2.83) compared to conservatives. However, there was no significant difference in awareness between moderates and conservatives. Study findings point to an association between individuals’ political ideology and HPV awareness. Further research is needed to understand the intricacies on how political ideology impacts HPV awareness. Overall, results highlight the need to incorporate individuals’ political ideologies in interventions geared toward increasing the awareness and uptake of HPV vaccination.
SARS‐CoV‐2 in digital era: Diagnostic techniques and importance of nucleic acid quantification with digital PCRs

Studies related to clinical diagnosis and research of SARS‐CoV‐2 are important in the current pandemic era. Although molecular biology has emphasised the importance of qualitative analysis, quantitative analysis with nucleic acids in relation to SARS‐CoV‐2 needs to be clearly emphasised, which can provide perspective for viral dynamic studies of SARS‐CoV‐2. In this regard, the requirement and utilization of digital PCR in COVID‐19 research has substantially increased during the pandemic, necessitating the aggregation of its cardinal applications and future scopes. Hence, this meta‐review comprehensively addresses and emphasises the importance of nucleic acid quantification of SARS‐CoV‐2 RNA with digital PCR (dPCR). Various quantitative techniques of clinical significance like immunological, proteomic and nucleic acid‐based diagnosis and quantification, have been comparatively discussed. Furthermore, the core part of the article focusses on the working principle and advantages of digital PCR, along with its applications in COVID‐19 research. Several important applications like viral load quantitation, environmental surveillance and assay validation have been extensively investigated and discussed. Certain key future scopes of clinical importance, like mortality prediction, viral/variant‐symbiosis, and antiviral studies were also identified, suggesting several possible digital PCR applications in COVID‐19 research.
Short-acting versus long-acting opioids for pediatric postoperative pain management

ABSTRACT Introduction Opioids are potent analgesics commonly used to manage children’s moderate to severe perioperative pain in children. A wide range of short and long-acting opioids are used to treat surgical pain and will be reviewed in this article. Areas covered Both short- and long-acting opioids contain unique therapeutic benefits and adverse effects; however, due to the side effect profile and safety concerns, lack of familiarity, and evidence with long-acting opioids to treat surgical pain, shorter-acting opioids have traditionally been used in children. Almost all opioids work by binding to the mu receptor. Methadone, a long-acting opioid, is an exception because it also has beneficial N-methyl-D-aspartate antagonist properties. Clinically methadone’s properties could translate to improved analgesic outcomes, reduced risk of adverse events, less risk for acute hyperalgesia, tolerance and abuse potential, faster recovery, and reduced risk for chronic persistent surgical pain. This review article summarizes and compares the evidence of commonly used short and long-acting opioids for perioperative pain control in the pediatric population. Expert opinion Individualized methadone therapy using pharmacogenomics has the potential to transform opioid use in pain management by improving patient safety and analgesic outcomes, thereby addressing the gaps in current standardized ERAS protocols.
The clinical and cost effectiveness of remote expert wound nurse consultation for healing of pressure injuries among residential aged care patients: A protocol for a prospective pilot parallel cluster randomised controlled trial

Pressure injuries affect 1 to 46% of residents in aged care (long term) facilities and cause a substantial economic burden on health care systems. Remote expert wound nurse consultation has the potential to improve pressure injury outcomes; however, the clinical and cost effectiveness of this intervention for healing of pressure injuries in residential aged care require further investigation. We describe the remote expert wound nurse consultation intervention and the method of a prospective, pilot, cluster randomised controlled trial. The primary outcome is number of wounds healed. Secondary outcomes are wound healing rate, time to healing, wound infection, satisfaction, quality of life, cost of treatment and care, hospitalisations, and deaths. Intervention group participants receive the intervention over a 12‐week period and all participants are monitored for 24 weeks. A wound imaging and measurement system is used to analyse pressure injury images. A feasibility and fidelity evaluation will be concurrently conducted. The results of the trial will inform the merit of and justification for a future definitive trial to evaluate the clinical and cost effectiveness of remote expert wound nurse consultation for the healing of pressure injuries in residential aged care.
Correlation of vitamin D receptor genotypes, specific IgE levels and other variables with asthma control in children

Abstract Introduction Asthma is a common condition affecting millions of children globally. The main goal of this study is to assess factors related to asthma management, particularly atopy level and the impact of genetic variants of the vitamin D receptor (VDR) gene. Methods Asthmatic children were enrolled in an outpatient respiratory clinic. Information on patients’ medication adherence, medical and medication factors, and sociodemographic were gathered. Spirometry FEV1% and FVC% measurements, and the asthma control test were used to evaluate the severity of asthma, and genotyping of the VDR gene and radioallergosorbent test (RAST) were conducted. Regression analyses were conducted to evaluate variables associated with asthma control and spirometry measures. Results A total of 313 participants (67.4% males) were recruited in the current study. The mean age was 9.37 (±3.45) years. The mean score for adherence was 4.26 (±2.52), and only 46% of the participants had controlled asthma. Forward conditional stepwise binary regression showed that low and moderate Inhaled corticosteroids (ICS) dose (OR= 0.42 (95% CI 0.20–0.90), p = 0.026; OR = 0.371 (95% CI 0.2–0.72), p = 0.003, respectively) decreased the odds of being in the controlled asthma group, while higher inhaler score (OR = 2.75 (95% CI 2.17–3.49, p < 0.001)) increased the odds of being in the controlled asthma group. However, results found no association between VDR genotype and asthma control, spirometry values or hospitalization due to asthma. Conclusions The results indicated that many of the asthma patients had poorly controlled asthma. Factors that were associated with poor asthma control included poor inhaler technique.
Reappraisal of the velocity derivative flatness factor in various turbulent flows

We first analytically show, starting with the Navier–Stokes equations, that the value of the derivative flatness is controlled by pressure diffusion of energy, viscous destructive effects and large-scale effects (decay and/or production). The latter two terms tend to zero when the Taylor-microscale Reynolds number $Re_{\unicode[STIX]{x1D706}}$ is sufficiently large. We argue that the pressure-diffusion term should also tend to a constant at large $Re_{\unicode[STIX]{x1D706}}$ . Available data for the velocity derivative flatness, $F$ , in different turbulent flows are re-examined and interpreted in the light of the finite-Reynolds-number effect. It is found that $F$ can differ from flow to flow at moderate $Re_{\unicode[STIX]{x1D706}}$ ; for a given flow, $F$ may also depend on the initial conditions. The data for $F$ in various flows, e.g. along the axis in the far field of plane and circular jets, and grid turbulence, show that it approaches a constant, with a value slightly larger than 10, when $Re_{\unicode[STIX]{x1D706}}$ is sufficiently large. This behaviour for $F$ is supported, at least qualitatively, by our analytical considerations. The constancy of $F$ at large $Re_{\unicode[STIX]{x1D706}}$ violates the refined similarity hypothesis introduced by Kolmogorov (J. Fluid Mech., vol. 13, 1962, pp. 82–85) to account for the intermittency of the energy dissipation rate. It is not, however, inconsistent with Kolmogorov’s original similarity hypothesis (Dokl. Akad. Nauk SSSR, vol. 30, 1941, pp. 299–303), although we contend that the power-law relation $F\sim Re_{\unicode[STIX]{x1D706}}^{\unicode[STIX]{x1D6FC}_{4}}$ (Kolmogorov 1962), which is widely accepted in the literature, has in reality been almost invariably used to ‘model’ the finite-Reynolds-number effect for the laboratory data and has been strongly influenced by the weighting given to the atmospheric surface layer data. The inclusion of the latter data has misled previous investigations of how $F$ varies with $Re_{\unicode[STIX]{x1D706}}$ .
The Physics of the Hume-Rothery Electron Concentration Rule

For a long time we have shared the belief that the physics of the Hume-Rothery electron concentration rule can be deepened only through thorough investigation of the interference phenomenon of itinerant electrons with a particular set of lattice planes, regardless of whether d-states are involved near the Fermi level or not. For this purpose, we have developed the FLAPW-Fourier theory (Full potential Linearized Augmented Plane Wave), which is capable of determining the square of the Fermi diameter, ( 2 k F ) 2 , and the number of itinerant electrons per atom, e/a, as well as the set of lattice planes participating in the interference phenomenon. By determining these key parameters, we could test the interference condition and clarify how it contributes to the formation of a pseudogap at the Fermi level. Further significant progress has been made to allow us to equally handle transition metal (TM) elements and their compounds. A method of taking the center of gravity energy for energy distribution of electrons with a given electronic state has enabled us to eliminate the d-band anomaly and to determine effective ( 2 k F ) 2 , and e/a, even for systems involving the d-band or an energy gap across the Fermi level. The e/a values for 54 elements covering from Group 1 up to Group 16 in the Periodic Table, including 3d-, 4d- and 5d-elements, were determined in a self-consistent manner. The FLAPW-Fourier theory faces its limit only for elements in Group 17 like insulating solids Cl and their compounds, although the value of e/a can be determined without difficulty when Br becomes metallic under high pressures. The origin of a pseudogap at the Fermi level for a large number of compounds has been successfully interpreted in terms of the interference condition, regardless of the bond-types involved in the van Arkel-Ketelaar triangle map.
Ultrafast destructuring of laser-irradiated tungsten: Thermal or nonthermal process

The time needed for metals to respond structurally to electronic excitation is usually considered to be set by picosecond-long electron-phonon coupling, hence limiting the rapid achievement of structural phase changes. Via time-resolved ellipsometry, we show that fs laser excitation of tungsten determines unexpectedly fast optical and structural transformations, almost on the time scale of the laser pulse, with sub-ps destructuring of matter. If at low energies, below the damage threshold, Fermi redistribution within the $d$-band pseudogap populates localized states that screen ions, in ablative ranges, at electronic energies above 4 eV, a charge deficit appears on bonding orbitals generating bond-softening and ionic repulsion in spite of the metallic environment. Along pressure gradients, this rapidly destabilizes the structure. Sub-ps ablation occurs, challenging thermally driven scenarios of phonon activation. First-principles and hydrodynamic models show that ablation interrelates nonclassical charge distortions, electronic stress, and classical electron-phonon dynamics, with the coexistence of thermal and nonthermal effects on scales believed to be prohibitive for the former.
Adapting Knowledge for Few-shot Table-to-Text Generation

Pretrained language models (PLMs) have made remarkable progress in table-to-text generation tasks. However, the lack of domain-specific knowledge makes it challenging to bridge the topological gap between tabular data and text, especially in real-world applications with limited resources. To mitigate the limitation of insufficient labeled data, we propose a novel framework: Adapt-Knowledge-to-Generate (AKG). The core insight of AKG is to adapt unlabeled domain-specific knowledge into the model, which brings at least three benefits: (1) it injects representation of normal table-related descriptions to bridge the topological gap between tabular data and texts; (2) it enables us to use large amounts of unlabeled domain-specific knowledge fully, which can alleviate the PLMs' inherent shortcomings of lacking domain knowledge; (3) it allows us to design various tasks to employ the domain-specific knowledge. Extensive experiments and analyses are conducted on three open-domain, few-shot natural language generation (NLG) data sets: Humans, Songs, and Books. Compared to previous state-of-the-art approaches, our model achieves superior performance in terms of both fluency and accuracy as judged by human and automatic evaluations.
Lieb-Schultz-Mattis anomalies and web of dualities induced by gauging in quantum spin chains

Lieb-Schultz-Mattis (LSM) theorems impose non-perturbative constraints on the zero-temperature phase diagrams of quantum lattice Hamiltonians (always assumed to be local in this paper). LSM theorems have recently been interpreted as the lattice counterparts to mixed ’t Hooft anomalies in quantum field theories that arise from a combination of crystalline and global internal symmetry groups. Accordingly, LSM theorems have been reinterpreted as LSM anomalies. In this work, we provide a systematic diagnostic for LSM anomalies in one spatial dimension. We show that gauging subgroups of the global internal symmetry group of a quantum lattice model obeying an LSM anomaly delivers a dual quantum lattice Hamiltonian such that its internal and crystalline symmetries mix non-trivially through a group extension. This mixing of crystalline and internal symmetries after gauging is a direct consequence of the LSM anomaly, i.e., it can be used as a diagnostic of an LSM anomaly. We exemplify this procedure for a quantum spin-1/2 chain obeying an LSM anomaly resulting from combining a global internal \mathbb{Z}^{\,}_{2}×\mathbb{Z}^{\,}_{2}ℤ2×ℤ2 symmetry with translation or reflection symmetry. We establish a triality of models by gauging a \mathbb{Z}^{\,}_{2}\subset\mathbb{Z}^{\,}_{2}×\mathbb{Z}^{\,}_{2}ℤ2⊂ℤ2×ℤ2 symmetry in two ways, one of which amounts to performing a Kramers-Wannier duality, while the other implements a Jordan-Wigner duality. We discuss the mapping of the phase diagram of the quantum spin-1/2 XYZXYZ chains under such a triality. We show that the deconfined quantum critical transitions between Neel and dimer orders are mapped to either topological or conventional Landau-Ginzburg transitions. Finally, we extend our results to \mathbb{Z}^{\,}_{n}ℤn clock models with \mathbb{Z}^{\,}_{n}×\mathbb{Z}^{\,}_{n}ℤn×ℤn global internal symmetry, and provide a reinterpretation of the dual internal symmetries in terms of \mathbb{Z}^{\,}_{n}ℤn charge and dipole symmetries.
ON POSITIVTY

We generalize the positivity conjecture on (Kauffman bracket) skein algebras to Roger–Yang skein algebras. To generalize it, we use Chebyshev polynomials of the first kind to give candidates of positive bases. Moreover, the polynomials form a lower bound in the sense of [Lˆe18] and [LTY21]. We also discuss a relation between the polynomials and the centers of Roger–Yang skein algebras when the quantum parameter is a complex root of unity.
Moment tracking and their coordinate transformations for macroparticles with an application to plasmas around black holes

Particle-in-cell (PIC) codes usually represent large groups of particles as a single macroparticle. These codes are computationally efficient but lose information about the internal structure of the macroparticle. To improve the accuracy of these codes, this work presents a method in which, as well as tracking the macroparticle, the moments of the macroparticle are also tracked. Although the equations needed to track these moments are known, the coordinate transformations for moments where the space and time coordinates are mixed cannot be calculated using the standard method for representing moments. These coordinate transformations are important in astrophysical plasma, where there is no preferred coordinate system. This work uses the language of Schwartz distributions to calculate the coordinate transformations of moments. Both the moment tracking and coordinate transformation equations are tested by modelling the motion of uncharged particles in a circular orbit around a black hole in both Schwarzschild and Kruskal–Szekeres coordinates. Numerical testing shows that the error in tracking moments is small, and scales quadratically. This error can be improved by including higher order moments. By choosing an appropriate method for using these moments to deposit the charge back onto the grid, a full PIC code can be developed.
Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification

Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis. However, traditional methods for object ReID directly adopting CNN models trained on the ImageNet dataset usually produce unsatisfactory retrieval performance on colonoscopic datasets due to the large domain gap. Additionally, these methods neglect to explore the potential of self-discrepancy among intra-class relations in the colonoscopic polyp dataset, which remains an open research problem in the medical community. To solve this dilemma, we propose a simple but effective training method named Colo-ReID, which can help our model learn more general and discriminative knowledge based on the meta-learning strategy in scenarios with fewer samples. Based on this, a dynamic Meta-Learning Regulation mechanism called MLR is introduced to further boost the performance of polyp re-identification. To the best of our knowledge, this is the first attempt to leverage the meta-learning paradigm instead of traditional machine learning algorithm to effectively train deep models in the task of colonoscopic polyp re-identification. Empirical results show that our method significantly outperforms current state-of-the-art methods by a clear margin.
ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces

The prominent progress in generative models has significantly improved the reality of generated faces, bringing serious concerns to society. Since recent GAN-generated faces are in high realism, the forgery traces have become more imperceptible, increasing the forensics challenge. To combat GAN-generated faces, many countermeasures based on Convolutional Neural Networks (CNNs) have been spawned due to their strong learning ability. In this paper, we rethink this problem and explore a new approach based on forest models instead of CNNs. Specifically, we describe a simple and effective forest-based method set called {\em ForensicsForest Family} to detect GAN-generate faces. The proposed ForensicsForest family is composed of three variants, which are {\em ForensicsForest}, {\em Hybrid ForensicsForest} and {\em Divide-and-Conquer ForensicsForest} respectively. ForenscisForest is a newly proposed Multi-scale Hierarchical Cascade Forest, which takes semantic, frequency and biology features as input, hierarchically cascades different levels of features for authenticity prediction, and then employs a multi-scale ensemble scheme that can comprehensively consider different levels of information to improve the performance further. Based on ForensicsForest, we develop Hybrid ForensicsForest, an extended version that integrates the CNN layers into models, to further refine the effectiveness of augmented features. Moreover, to reduce the memory cost in training, we propose Divide-and-Conquer ForensicsForest, which can construct a forest model using only a portion of training samplings. In the training stage, we train several candidate forest models using the subsets of training samples. Then a ForensicsForest is assembled by picking the suitable components from these candidate forest models.
Partial Label Supervision for Agnostic Generative Noisy Label Learning

Noisy label learning has been tackled with both discriminative and generative approaches. Despite the simplicity and efficiency of discriminative methods, generative models offer a more principled way of disentangling clean and noisy labels and estimating the label transition matrix. However, existing generative methods often require inferring additional latent variables through costly generative modules or heuristic assumptions, which hinder adaptive optimisation for different causal directions. They also assume a uniform clean label prior, which does not reflect the sample-wise clean label distribution and uncertainty. In this paper, we propose a novel framework for generative noisy label learning that addresses these challenges. First, we propose a new single-stage optimisation that directly approximates image generation by a discriminative classifier output. This approximation significantly reduces the computation cost of image generation, preserves the generative modelling benefits, and enables our framework to be agnostic in regards to different causality scenarios (i.e., image generate label or vice-versa). Second, we introduce a new Partial Label Supervision (PLS) for noisy label learning that accounts for both clean label coverage and uncertainty. The supervision of PLS does not merely aim at minimising loss, but seeks to capture the underlying sample-wise clean label distribution and uncertainty. Extensive experiments on computer vision and natural language processing (NLP) benchmarks demonstrate that our generative modelling achieves state-of-the-art results while significantly reducing the computation cost. Our code is available at https://github.com/lfb-1/GNL.