Miracle-dz/UniFAR-FTUs huggingface dataset:
Participatory Methods to Improve and Develop Pediatric Nursing Practice: A Scoping Review

ABSTRACT Children’s nurses in African pediatric settings are often responsible for leading practice improvements. There is a shortage of contextually relevant guidance to inform the design of practice improvement projects in African care settings. Distinctive features of children’s nursing practice in Africa include high levels of family caregiver involvement, and organizational and professional cultures which value participation. While established practice improvement methods offer many strengths, methods developed in other geographies should not be adopted uncritically. Our purpose in undertaking this review was to inform selection of methods for a multi-center practice improvement project in Africa. Our aim was to identify types of participatory methods used to improve and develop pediatric nursing practice. We used the PRISMA-ScR method to conduct a scoping review to identify published reports of participatory methods used to improve and develop pediatric nursing practice. We undertook structured searches of five bibliographic databases to identify articles. Only articles written in the English language were included and no limitation was applied to publication date. We identified 7,406 titles and abstracts. After screening, 76 articles met the inclusion criteria. A wide range of participatory methodologies were identified; just under half (n = 34) reported on methods that were not recognized or named methodologies but can be described as collaborative in nature. Plan-do-study-act cycles were reported in 22 articles. There was considerable heterogeneity in frameworks, practical tools and/or nursing models on which the participatory methods were based and there was no apparent relationship between these and the choice of participatory methods. The outcomes identified were also heterogenous in nature and were grouped according to whether they improved structure and/or processes and patient outcomes. Most of the included articles stem from high-income countries with little evidence from low-middle-income countries and none in African settings. Less than half of the included articles involved family caregivers in their practice improvement methodologies. This review highlights the need for greater application of formalized methods for practice improvement and improved rigor and consistency in reporting outcomes. There is also a need to formalize participatory practice improvement methodologies specifically suited to Africa’s context of children’s nursing.
Motivating Proactive Biorisk Management

Scholars and practitioners of biosafety and biosecurity (collectively, biorisk management or BRM) have argued that life scientists should play a more proactive role in monitoring their work for potential risks, mitigating harm, and seeking help as necessary. However, most efforts to promote proactive BRM have focused on training life scientists in technical skills and have largely ignored the extent to which life scientists wish to use them (ie, their motivation). In this article, we argue that efforts to promote proactive BRM would benefit from a greater focus on life scientists' motivation. We review relevant literature on life scientists' motivation to practice BRM, offer examples of successful interventions from adjacent fields, and outline ideas for possible interventions to promote proactive BRM, along with strategies for iterative development, testing, and scaling.
Antitumor effect of selenium-rich Brazil nuts and selenomethionine dietary supplementation on pre-existing 4T1 mammary tumor growth in mice

Selenium (Se) is an essential micronutrient known to play an important role in the antioxidant system that can potentially influence tumor growth. We aimed to investigate the effects of dietary Se supplementation after detection of 4T1 mammary tumor growth in BALB/c mice. Thirty female mice received subcutaneous inoculation of 4T1 cells. After five days, all animals presenting palpable tumors were randomly assigned to three groups: a control group (Se-control) receiving a diet with adequate Se (0.15 mg/kg) and two other groups that received Se-supplemented diets (1.4 mg/kg of total Se) with either Brazilian nuts (Se-Nuts) or selenomethionine (SeMet). Data were assessed by either One or Two-way ANOVA followed by Tukey’s HSD or Bonferroni’s post hoc tests, respectively. Both Se-supplemented diets reduced tumor volume from the thirteenth day of feeding compared with the Se-adequate (control) diet (p < 0.05). The SeMet group presented a higher Se blood concentration (p < 0.05) than the Se-control group, with the Se-Nuts group presenting intermediate values. Selenoprotein P gene expression in the liver was higher in the Se-Nuts group than in the Se-control group (p < 0.05), while the SeMet group presented intermediate expression. Dietary Se supplementation, starting after detection of 4T1 palpable lesions, reduced tumor volume in mice.
Machine learning enables prediction of metabolic system evolution in bacteria

Evolution prediction is a long-standing goal in evolutionary biology, with potential impacts on strategic pathogen control, genome engineering, and synthetic biology. While laboratory evolution studies have shown the predictability of short-term and sequence-level evolution, that of long-term and system-level evolution has not been systematically examined. Here, we show that the gene content evolution of metabolic systems is generally predictable by applying ancestral gene content reconstruction and machine learning techniques to ~3000 bacterial genomes. Our framework, Evodictor, successfully predicted gene gain and loss evolution at the branches of the reference phylogenetic tree, suggesting that evolutionary pressures and constraints on metabolic systems are universally shared. Investigation of pathway architectures and meta-analysis of metagenomic datasets confirmed that these evolutionary patterns have physiological and ecological bases as functional dependencies among metabolic reactions and bacterial habitat changes. Last, pan-genomic analysis of intraspecies gene content variations proved that even “ongoing” evolution in extant bacterial species is predictable in our framework.
A shunt active power filter adopting Lyapunov control approach

Abstract The rising price of oil, the reduction of cheap energy sources, and growing global environmental concerns have drawn greater attention to suppressing harmonicrelated problems resulting from nonlinear load pollution like diode rectifiers in traditional ac drives. To solve these problems, the use of active power filters (APFs) to mitigate harmonic proliferation has drawn much attention in recent years. Most studies only show control block diagrams with classic control methods. These control schemes look very attractive for their simplicity and ease of implementation, but lack in presenting an adequate compensation algorithm or stability analysis of active power filters. In this paper, a Lyapunov‐based current control method for an APF system is proposed. The control law is derived from directly applying the Lyapunov stability theory to the integral controller, so that the closed‐loop systems possess better harmonic current elimination. The proposed APF controller forces the supply current to be sinusoidal, with low current harmonics, and to be in phase with the line voltage. Computer simulations and experimental results obtained from a laboratory system developed in this paper verify the viability and effectiveness of the full‐digital‐controlled active filter.
Older men, younger women: blesser–blessee relationships among university students in Durban, South Africa

Abstract In sub-Saharan Africa, sexual relationships between younger women and older men have been shown to pose a risk of HIV transmission. In South Africa, there has been growing interest in blesser–blessee relationships. This study explores perceptions of these relationships among university students in Durban, South Africa. The study draws on 15 in-depth interviews with students. Participants suggest that older men engage in these relationships for sexual gratification and younger women do so for monetary or material gain. Interviews revealed that participants believed that these relationships are common with both men and women engaging in them and putting themselves at risk of HIV infection. It is imperative to explore the context and motivation for these relationships because of their broader social and health implications. In addition, greater effort should be geared towards addressing the underlying factors that facilitate blesser–blessee relationships.
High Spatial Resolution Mid- and Far-infrared Imaging Study of NGC 2346

We present the first high spatial resolution mid-IR and far-IR images of the planetary nebula NGC 2346 using the Multiband Imaging Photometer for Spitzer (MIPS) on board the Spitzer Space Telescope. The morphology at 24 μm is strikingly similar to that seen in the optical image, with an additional unresolved hot dust component at the center. The 70 μm image reveals a torus-like structure near the narrow waist of the bipolar nebula, while the 160 μm image shows a very extended cold dust envelope around the nebula. Our new MIPS data provide direct evidence that the mass loss in the end of the asymptotic giant branch (AGB) phase diverts from spherical symmetry to axisymmetry and that the binary central star has played an important role in the bipolar nebular evolution.
Immunogenicity and safety of a quadrivalent meningococcal conjugate vaccine (MenACYW-TT) administered as a booster to adults aged ≥59 years: A phase III randomized study

ABSTRACT This 2-stage Phase III study (NCT04142242) of a recently licensed quadrivalent meningococcal tetanus toxoid-conjugate vaccine (MenACYW-TT) assessed the safety and immunogenicity of a booster dose in older adults (≥59 years) primed with either MenACYW-TT or a quadrivalent meningococcal polysaccharide vaccine (MPSV4). Immune persistence of MenACYW-TT and MPSV4 after primary vaccination was also evaluated. During Stage I, the participants administered MPSV4 (n = 165) or MenACYW-TT (n = 236) 3 years previously were randomized 9:2 to receive either a MenACYW-TT booster or to have blood drawn for persistence only. Participants primed with MPSV4 or MenACYW-TT 6–7 years previously had blood drawn for antibody persistence only. A serum bactericidal assay using human complement was used to measure functional antibodies against each serogroup at baseline and, for those receiving a booster, 30 days post-vaccination (D30). Proportions of participants with seroresponse (post-vaccination titers ≥1:16 when baseline titers <1:8 or ≥ 4-fold increase when baseline titers ≥1:8) were determined. Safety data were collected up to D30. Seroresponse rates for all serogroups at D30 ranged from 49.2% to 60.8% in the MPSV4-primed group, and 79.3–93.1% in the MenACYW-TT-primed group. MenACYW-TT induced sufficient seroresponses in each primed group. Geometric mean titers (GMTs) for serogroups C, W, and Y remained or trended higher than pre-vaccination levels at both 3 and 6–7 years after primary vaccination, indicating immune persistence. Safety outcomes were comparable between groups. A MenACYW-TT booster was immunogenic and well tolerated in participants aged ≥59 years regardless of previous quadrivalent meningococcal vaccine received. The greatest immune responses occurred in those primed with MenACYW-TT.
International Collaboration on Palliative Care Development Between ASCO and the Land of Hornbills

PURPOSE Palliative care in Sarawak is mainly provided by health care professionals with limited formal training in palliative care. Therefore, in 2020, collaborative work between Sarawak General Hospital, University Malaysia Sarawak, and ASCO began. This study reports on the outcome of this collaboration. METHODS The collaboration was initiated with the first ASCO Palliative Care e-course, Train the Trainer program, International Development and Education Award—Palliative Care and translation of ASCO Palliative Care Interdisciplinary Curriculum resources. RESULTS This collaboration has resulted in the change of practice of palliative care among the oncology team of Sarawak General Hospital. CONCLUSION It encourages more timely palliative care referrals to ensure that patients with complex physical, psychosocial, and spiritual needs have the necessary input and support from the palliative care team throughout the course of patients’ illnesses.
First-principles thermal equation of state of fcc iridium

The thermal equation of states for fcc iridium (Ir) is obtained from ﬁrst-principles molecular dynamics up to 3000 K and 540 GPa. The equation of state (EoS) is globally ﬁtted to a simpliﬁed free energy model and various parameters are derived. The theoretical principal Hugoniot is compared with shockwave experiments, where discrepancy suggests formation of new Ir phases. A few representative EoS parameters, such as bulk modulus K T , thermal expansivity α , Gr¨uneisen parameter γ , and constant pressure capacity C P , Debye temperature, Θ D are computed to compare with experimental data.
Signed Directed Graph Contrastive Learning with Laplacian Augmentation

Graph contrastive learning has become a powerful technique for several graph mining tasks. It learns discriminative representation from different perspectives of augmented graphs. Ubiquitous in our daily life, singed-directed graphs are the most complex and tricky to analyze among various graph types. That is why singed-directed graph contrastive learning has not been studied much yet, while there are many contrastive studies for unsigned and undirected. Thus, this paper proposes a novel signed-directed graph contrastive learning, SDGCL. It makes two different structurally perturbed graph views and gets node representations via magnetic Laplacian perturbation. We use a node-level contrastive loss to maximize the mutual information between the two graph views. The model is jointly learned with contrastive and supervised objectives. The graph encoder of SDGCL does not depend on social theories or predefined assumptions. Therefore it does not require finding triads or selecting neighbors to aggregate. It leverages only the edge signs and directions via magnetic Laplacian. To the best of our knowledge, it is the first to introduce magnetic Laplacian perturbation and signed spectral graph contrastive learning. The superiority of the proposed model is demonstrated through exhaustive experiments on four real-world datasets. SDGCL shows better performance than other state-of-the-art on four evaluation metrics.
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction

Max sliced Wasserstein (Max-SW) distance has been widely known as a solution for less discriminative projections of sliced Wasserstein (SW) distance. In applications that have various independent pairs of probability measures, amortized projection optimization is utilized to predict the ``max"projecting directions given two input measures instead of using projected gradient ascent multiple times. Despite being efficient, Max-SW and its amortized version cannot guarantee metricity property due to the sub-optimality of the projected gradient ascent and the amortization gap. Therefore, we propose to replace Max-SW with distributional sliced Wasserstein distance with von Mises-Fisher (vMF) projecting distribution (v-DSW). Since v-DSW is a metric with any non-degenerate vMF distribution, its amortized version can guarantee the metricity when performing amortization. Furthermore, current amortized models are not permutation invariant and symmetric. To address the issue, we design amortized models based on self-attention architecture. In particular, we adopt efficient self-attention architectures to make the computation linear in the number of supports. With the two improvements, we derive self-attention amortized distributional projection optimization and show its appealing performance in point-cloud reconstruction and its downstream applications.
Heuristic for Diverse Kemeny Rank Aggregation based on Quantum Annealing

The Kemeny Rank Aggregation ( KRA ) problem is a well-studied problem in the ﬁeld of Social Choice with a variety of applications in many diﬀerent areas like databases and search engines. Intuitively, given a set of votes over a set of candidates, the problem asks to ﬁnd an aggregated ranking of candidates that minimizes the overall dissatisfac-tion concerning the votes. Recently, a diverse version of KRA was considered which asks for a suf-ﬁciently diverse set of suﬃciently good solutions. The framework of diversity of solutions is a young and thriving topic in the ﬁeld of artiﬁcial intelligence. The main idea is to provide the user with not just one, but with a set of diﬀerent solutions, enabling her to pick a suﬃciently good solution that satisﬁes additional subjective criteria that are hard or impossible to model. In this work, we use a quantum annealer to solve the KRA problem and to compute a representa-tive set of solutions. Quantum annealing is a meta search heuristic that does not only show promising runtime behavior on currently existing prototypes but also samples the solutions space in an inher-ently diﬀerent way, making use of quantum eﬀects. We describe how KRA instances can be solved by a quantum annealer and provide an implementation as well as experimental evaluations. As existing quantum annealers are still restricted in their number of qubits, we further implement two diﬀerent data reduction rules that can split an instance into a set of smaller instances. In our evaluation, we compare classical heuristics that allow to sample multiple solutions such as simulated annealing and local search with quantum annealing performed on a physical quantum annealer. We compare runtime, quality of solution, and diversity of solutions, with and without applying preceding data reduction rules. While our aim is to compute a diverse set of solutions, we also include classical heuristics that only compute a single solution in our experiments.
SACDNet: Towards Early Type 2 Diabetes Prediction with Uncertainty for Electronic Health Records

Type 2 diabetes mellitus (T2DM) is one of the most common diseases and a leading cause of death. The problem of early diagnosis of T2DM is challenging and necessary to prevent serious complications. This study proposes a novel neural network architecture for early T2DM prediction using multi-headed self-attention and dense layers to extract features from historic diagnoses, patient vitals, and demographics. The proposed technique is called the Self-Attention for Comorbid Disease Net (SACDNet), achieving an accuracy of 89.3% and an F1-Score of 89.1%, having a 1.6% increased accuracy and 1.3% increased f1-score compared to the baseline techniques. Monte Carlo (MC) Dropout is applied to the SACDNet to get a bayesian approximation. A T2DM prediction framework based on the MC Dropout SACDNet is proposed to quantize the uncertainty associated with the predictions. A T2DM prediction dataset is also built as part of this study which is based on real-world routine Electronic Health Record (EHR) data comprising 4,124 diabetic and 181,767 non-diabetic examples, collected from 295 different EHR systems running in different parts of the United States of America. This dataset is further used to evaluate 7 different machine learning and 3 deep learning-based models. Finally, a detailed analysis of the fairness of every technique against different patient demographic groups is performed to validate the unbiased generalization of the techniques and the diversity of the data.
Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History

Throughout the history of art, the pose—as the holistic abstraction of the human body’s expression—has proven to be a constant in numerous studies. However, due to the enormous amount of data that so far had to be processed by hand, its crucial role to the formulaic recapitulation of art-historical motifs since antiquity could only be highlighted selectively. This is true even for the now automated estimation of human poses, as domain-specific, sufficiently large data sets required for training computational models are either not publicly available or not indexed at a fine enough granularity. With the Poses of People in Art data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators. It consists of 2 , 454 images from 22 art-historical depiction styles, including those that have increasingly turned away from lifelike representations of the body since the 19 th century. A total of 10 , 749 human figures are precisely enclosed by rectangular bounding boxes, with a maximum of four per image labeled by up to 17 keypoints; among these are mainly joints such as elbows and knees. For machine learning purposes, the data set is divided into three subsets—training, validation, and testing—, that follow the established JSON-based Microsoft Common Objects in Context (COCO) format, respectively. Each image annotation, in addition to mandatory fields, provides metadata from the art-historical online encyclopedia WikiArt. With this paper, we elaborate on the acquisition and constitution of the data set, address various application scenarios, and discuss prospects for a digitally supported art history. We show that the data set enables the comprehensive investigation of body phenomena in art, whether at the level of individual figures, which can thus be captured in their subtleties, or entire figure constellations, whose position, distance, or proximity to one another is considered.
Data-centric AI: Perspectives and Challenges

The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability. Although our community has continuously invested efforts into enhancing data in different aspects, they are often isolated initiatives on specific tasks. To facilitate the collective initiative in our community and push forward DCAI, we draw a big picture and bring together three general missions: training data development, inference data development, and data maintenance. We provide a top-level discussion on representative DCAI tasks and share perspectives. Finally, we list open challenges. More resources are summarized at https://github.com/daochenzha/data-centric-AI
Secular instability of rotating Newtonian stars.

The effect of gravitational radiation and of viscosity on the stability of rotating self-gravitating fluids is considered. Previous cirteria governing secular stability to radiation are shown to fail as a result of the trival displacements introduced in a previous paper (1). The required modification is obtained by describing perturbations in terms of canonical displacements (displacements orthogonal to the trivials). There nevertheless remain physical perturbations having angular dependence e/sup i/mphi which, for sufficiently large m, make the stability functional (the canonical energy E/sub c/) negative, and it follows that all rotating stars are unstable or marginally unstable to gravitational radiation. In the case of stability against viscosity, the corresponding stability criterion is shown to involve the the canonical energy in a rotating frame, E/sub c/,R, a functional invariant under gauge transformations associated with the trival displacements. By using the functional E/sub c/,R to analyze local stability, it is found that a star is locally stable against viscosity if and only if the special entropy increases outward (in the sense of decreasing pressure). Finally, the behavior of normal modes is discussed and used to elucidate the generic radiation-induced instability; and certain orthogonality properties are derived.