Miracle-dz/UniFAR-FTUs huggingface dataset:
Shared understanding and task-interdependence in nursing interns’ collaborative relations: A social network study of vocational health care internships in the Netherlands

ABSTRACT Shared understanding among collaborators is a key element of delivering successful interprofessional care and a main challenge for professional education concerns nurturing such understanding among students. We assessed how nursing students perceived different levels of shared understanding in their collaborations with others in clinical internships. We analyse the collaborative networks of interns to examine whether individual factors (attitudes, perceptions of collaborative cultures, and motivation) or relational factors among collaborators (task-interdependence, cooperation frequency, and interprofessional and hierarchical roles) affect shared understanding among 150 Dutch nursing interns and their collaborators (n = 865). Theoretically, we stress the importance of focusing on collaborative relations in interprofessional care settings. Multilevel models distinguish two levels in explaining the variation in shared understanding, nesting collaborative relationships within individuals. Results indicate merely 37.4% of found variation of shared understanding could be attributed to individual-level factors (variation between interns), while 62.6% of variation is found within interns, showing that shared understanding differs substantially between the collaborations one intern engages in. Multilevel models reveal that task-interdependence strongly predicts shared understanding in inter- and intraprofessional collaborations. We conclude that focusing on collaborative relations is essential to foster shared understanding in vocational internship programmes, and that health care organisations should pay explicit attention to task-interdependence in interns’ collaborations.
Safety, tolerability, and efficacy of maralixibat in adults with primary sclerosing cholangitis: Open-label pilot study

Background: Primary sclerosing cholangitis (PSC) is frequently associated with pruritus, which significantly impairs quality of life. Maralixibat is a selective ileal bile acid transporter (IBAT) inhibitor that lowers circulating bile acid (BA) levels and reduces pruritus in cholestatic liver diseases. This is the first proof-of-concept study of IBAT inhibition in PSC. Methods: This open-label study evaluated the safety and tolerability of maralixibat ≤10 mg/d for 14 weeks in adults with PSC. Measures of pruritus, biomarkers of BA synthesis, cholestasis, and liver function were also assessed. Results: Of 27 enrolled participants, 85.2% completed treatment. Gastrointestinal treatment-emergent adverse events (TEAEs) occurred in 81.5%, with diarrhea in 51.9%. TEAEs were mostly mild or moderate (63.0%); 1 serious TEAE (cholangitis) was considered treatment related. Mean serum BA (sBA) levels decreased by 16.7% (−14.84 µmol/L; 95% CI, −27.25 to −2.43; p = 0.0043) by week 14/early termination (ET). In participants with baseline sBA levels above normal (n = 18), mean sBA decreased by 40.0% (−22.3 µmol/L, 95% CI, −40.38 to −4.3; p = 0.004) by week 14/ET. Liver enzyme elevations were not significant; however, increases of unknown clinical significance in conjugated bilirubin levels were observed. ItchRO weekly sum scores decreased from baseline to week 14/ET by 8.4% (p = 0.0495), by 12.6% (p = 0.0275) in 18 participants with pruritus at baseline, and by 70% (p = 0.0078) in 8 participants with ItchRO daily average score ≥3 at baseline. Conclusions: Maralixibat was associated with reduced sBA levels in adults with PSC. In participants with more severe baseline pruritus, pruritus improved significantly from baseline. TEAEs were mostly gastrointestinal related. These results support further investigation of IBAT inhibitors for adults with PSC-associated pruritus. ClinicalTrials.gov: NCT02061540.
Investigation into Grief Experiences of the Bereaved During the Covid-19 Pandemic

The objective of the current study was to investigate the grief experiences of people affected by COVID-19. The study adopted a qualitative design of descriptive phenomenology. Fifteen adults who had lost a family member during the COVID-19 pandemic were selected as the sample through the purposive sampling method until theoretical saturation was achieved. Data was collected using semi-structured interviews and the Colaizzi analysis method. Six main themes (i.e., unexpressed grief, psychosomatic reactions, negative emotions, family problems, and social and occupational problems) were extracted. Data analysis showed that complex disenfranchised grief is the pervasive consequence of the COVID-19 experience. According to the findings, participants experienced disenfranchised grief during the loss of their loved ones due to the COVID-19 disease, which was a complex, painful experience accompanied by negative emotions and family, work, and social tensions. This grief is accompanied by more severe and prolonged symptoms, making it difficult for the bereaved to return to normal life. In unexpressed grieving, there are intense feelings of grief, pain, separation, despair, emptiness, low self-esteem, bitterness, or longing for the presence of the deceased. This grief originated from the conditions of quarantine and physical distance on the one hand, which required the control of the outbreak of the COVID-19 disease, and on the other hand, the cultural-religious context of the Iranian people.
An update on current and emerging drug treatments for idiopathic pulmonary fibrosis

ABSTRACT Introduction Idiopathic Pulmonary Fibrosis (IPF) is a progressive and devastating lung disease, characterized by progressive lung scarring. Areas covered Prior to antifibrotic therapy (pirfenidone and nintedanib), there was no validated pharmaceutical therapy for IPF. Both antifibrotics can slow disease progression; however, IPF remains a detrimental disease with poor prognosis and treatment survival rates of less than 7 years from diagnosis. Despite their effect the disease remains non-reversible and progressing whilst their side effect profile is often challenging. Treatment of comorbidities is also crucial. In this review, we discuss the current pharmacological management as well as management of comorbidities and symptoms. We also reviewed clinicaltrials.gov and summarized all the mid- to late-stage clinical trials (phase II and III) registered in IPF over the last 7 years and discuss the most promising drugs in clinical development. Expert opinion Future for IPF management will need to focus on current unresolved issues. First a primary pathogenetic pathway has not been clearly identified. Future management may involve a combination of the brushstroke approach with antifibrotics with targeted treatments for specific pathways in patient subsets following an ‘oncological’ approach. Another unmet need is the management of exacerbations, which are deadly in most cases, as well as either treating or preventing lung cancer.
A Meta-Analysis of The Prevalence and Etiology of Infertility in Iran

Infertility is a serious health issue in the world affecting approximately 8-10% of couples worldwide. The meta-analysis study was performed to assess the prevalence and etiologic factors associated with infertility in Iran. We made sure that the implementation of study and reporting the results were consistent with the MOOSE and PRISMA guidelines, respectively. All stages of the research were conducted by two authors, and the disagreement at each stage of the research was resolved by consensus. On January 1, 2020, we started a detailed literature search on international online databases, and Iranian Online databases, as well as specialized journals, several authentic international publishers and Google Scholar. We reviewed the reference list of identified articles for missed articles and then searched online for them. Data analysis was performed to estimate the prevalence using a random effects model. The lifetime infertility prevalence was found to be 11.3% [95% confidence interval (CI): 8.6-14.7] and the current infertility was evaluated to be 3.7% (95% CI: 3.2-4.3). The prevalence of primary infertility (based on 45 articles consisting of 51,021 samples) as well as secondary infertility (based on 13 articles consisting of 35,683 samples) in Iran were estimated to be 18.3% (95% CI: 15.4-21.6) and 2.5% (95% CI: 1.6-4.0), respectively. The prevalence of female, male, both and unexplained causes was estimated to be 32.0% (95% CI: 27.6-36.8), 43.3% (95% CI: 38.2-48.6), 12.5% (95% CI: 9.6-16.2) and 13.6% (95% CI: 10.2-17.8), respectively. The prevalence of causes related to ovulation, uterine tubes, and endometriosis in infertile women was estimated to be 54.0% (95% CI: 45.6-62.2), 15.5% (95% CI: 11.3-21.0), 6.2% (95% CI: 3.5-10.6), and 5.4% (95% CI: 2.5-11.3), respectively. In summary, the estimate of infertility burden in Iran did not change between 1990 and 2017 and its prevalence remains high. This research presents a unified and up-to-date overview regarding the burden of infertility in Iran.
TOWARDS A NEW VIEWPOINT ON CAUSALITY FOR TIME SERIES

Causation between time series is a most important topic in econometrics, financial engineering, biological and psychological sciences, and many other fields. A new setting is introduced for examining this rather abstract concept. The corresponding calculations, which are much easier than those required by the celebrated Granger-causality, do not necessitate any deterministic or probabilistic modeling. Some convincing computer simulations are presented.
Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks

Meta learning has attracted much attention recently in machine learning community. Contrary to conventional machine learning aiming to learn inherent prediction rules to predict labels for new query data, meta learning aims to learn the learning methodology for machine learning from observed tasks, so as to generalize to new query tasks by leveraging the meta-learned learning methodology. In this study, we interpret such learning methodology as learning an explicit hyper-parameter prediction function shared by all training tasks. Specifically, this function is represented as a parameterized function called meta-learner, mapping from a training/test task to its suitable hyper-parameter setting, extracted from a pre-specified function set called meta learning machine. Such setting guarantees that the meta-learned learning methodology is able to flexibly fit diverse query tasks, instead of only obtaining fixed hyper-parameters by many current meta learning methods, with less adaptability to query task's variations. Such understanding of meta learning also makes it easily succeed from traditional learning theory for analyzing its generalization bounds with general losses/tasks/models. The theory naturally leads to some feasible controlling strategies for ameliorating the quality of the extracted meta-learner, verified to be able to finely ameliorate its generalization capability in some typical meta learning applications, including few-shot regression, few-shot classification and domain generalization.
Hierarchical Aligned Multimodal Learning for NER on Tweet Posts

Mining structured knowledge from tweets using named entity recognition (NER) can be beneficial for many downstream applications such as recommendation and intention under standing. With tweet posts tending to be multimodal, multimodal named entity recognition (MNER) has attracted more attention. In this paper, we propose a novel approach, which can dynamically align the image and text sequence and achieve the multi-level cross-modal learning to augment textual word representation for MNER improvement. To be specific, our framework can be split into three main stages: the first stage focuses on intra-modality representation learning to derive the implicit global and local knowledge of each modality, the second evaluates the relevance between the text and its accompanying image and integrates different grained visual information based on the relevance, the third enforces semantic refinement via iterative cross-modal interactions and co-attention. We conduct experiments on two open datasets, and the results and detailed analysis demonstrate the advantage of our model.
Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning

Designing an effective representation learning method for multimodal sentiment analysis tasks is a crucial research direction. The challenge lies in learning both shared and private information in a complete modal representation, which is difficult with uniform multimodal labels and a raw feature fusion approach. In this work, we propose a deep modal shared information learning module based on the covariance matrix to capture the shared information between modalities. Additionally, we use a label generation module based on a self-supervised learning strategy to capture the private information of the modalities. Our module is plug-and-play in multimodal tasks, and by changing the parameterization, it can adjust the information exchange relationship between the modes and learn the private or shared information between the specified modes. We also employ a multi-task learning strategy to help the model focus its attention on the modal differentiation training data. We provide a detailed formulation derivation and feasibility proof for the design of the deep modal shared information learning module. We conduct extensive experiments on three common multimodal sentiment analysis baseline datasets, and the experimental results validate the reliability of our model. Furthermore, we explore more combinatorial techniques for the use of the module. Our approach outperforms current state-of-the-art methods on most of the metrics of the three public datasets.
Is cosmic birefringence model-dependent?

Exciting clues to isotropic cosmic birefringence have recently been detected in the EB cross-power spectra of the polarization data of the cosmic microwave background (CMB). Early Dark Energy (EDE) models with a pseudoscalar field coupled to photons via a Chern-Simons term can be used to explain this phenomenon, and can also potentially be used to simultaneously resolve the H 0 tension. In this work we incorporate an early dark energy scalar field, including a Chern-Simons coupling, into an existing Boltzmann solver and numerically recover the EB cross-power spectrum for two models in the literature; the α-attractor, and the Rock `n' Roll field. We find that both the models fit the EB spectra, and the EB spectra alone do not possess sufficient constraining power to distinguish the two models based on current data.
Optimal signal propagation in ResNets through residual scaling

Residual networks (ResNets) have significantly better trainability and thus performance than feed-forward networks at large depth. Introducing skip connections facilitates signal propagation to deeper layers. In addition, previous works found that adding a scaling parameter for the residual branch further improves generalization performance. While they empirically identified a particularly beneficial range of values for this scaling parameter, the associated performance improvement and its universality across network hyperparameters yet need to be understood. For feed-forward networks (FFNets), finite-size theories have led to important insights with regard to signal propagation and hyperparameter tuning. We here derive a systematic finite-size theory for ResNets to study signal propagation and its dependence on the scaling for the residual branch. We derive analytical expressions for the response function, a measure for the network's sensitivity to inputs, and show that for deep networks the empirically found values for the scaling parameter lie within the range of maximal sensitivity. Furthermore, we obtain an analytical expression for the optimal scaling parameter that depends only weakly on other network hyperparameters, such as the weight variance, thereby explaining its universality across hyperparameters. Overall, this work provides a framework for theory-guided optimal scaling in ResNets and, more generally, provides the theoretical framework to study ResNets at finite widths.
Measuring Cross-Lingual Transferability of Multilingual Transformers on Sentence Classification

Recent studies have exhibited remarkable capabilities of pre-trained multilingual Transformers, especially cross-lingual transferability. However, current methods do not measure cross-lingual transferability well, hindering the understanding of multilingual Transformers. In this paper, we propose IGap, a cross-lingual transferability metric for multilingual Transformers on sentence classification tasks. IGap takes training error into consideration, and can also estimate transferability without end-task data. Experimental results show that IGap outperforms baseline metrics for transferability measuring and transfer direction ranking. Besides, we conduct extensive systematic experiments where we compare transferability among various multilingual Transformers, fine-tuning algorithms, and transfer directions. More importantly, our results reveal three findings about cross-lingual transfer, which helps us to better understand multilingual Transformers.
Bayesian Change Detection in the Growing Window Recursive Strategy

A novel growing-window recursive algorithm for stochastic system change detection is derived based on the Bayesian inference principle. Model based detectors can be formalized by two concepts in literature: (a) working in a sliding-window strategy because of time-dependent computational complexity, or (b) running in parallel, each one matched to a certain assumption on a change point. This motivates us to investigate a more refined approach which utilizes all relevant data to catch the next change point. The basic idea is to formulate a distance measure between two probabilities, one confirming the change occurrence and the other confirming no change in the system behavior. This study aims to solve the difficulty of sliding time arguments in the compared probabilities as new data are sequentially obtained. The outcome of this analysis is an algorithm that recognizes the time and magnitude of the change occurrence.
The Geometry of Multilingual Language Models: An Equality Lens

Understanding the representations of different languages in multilingual language models is essential for comprehending their cross-lingual properties, predicting their performance on downstream tasks, and identifying any biases across languages. In our study, we analyze the geometry of three multilingual language models in Euclidean space and find that all languages are represented by unique geometries. Using a geometric separability index we find that although languages tend to be closer according to their linguistic family, they are almost separable with languages from other families. We also introduce a Cross-Lingual Similarity Index to measure the distance of languages with each other in the semantic space. Our findings indicate that the low-resource languages are not represented as good as high resource languages in any of the models
Crystalline phases and devil's staircase in qubit spin ice

Motivated by the recent realization of an artificial quantum spin ice in an array of superconducting qubits with tunable parameters [King {\it et al.}, Science 373, 576 (2021)], we scrutinize a quantum six vertex model on the square lattice that distinguishes type-I and type-II vertices. We map the zero-temperature phase diagram using numerical (exact diagonalization) and analytical (perturbation expansion, Gerschgorin theorem) methods. Following a symmetry classification, we identify three crystalline phases alongside a subextensive manifold of isolated configurations. Monte Carlo simulations at the multicritical Rokhsar-Kivelson point provide evidence for a quantum phase exhibiting a cascade of transitions with increasing flux. By comparing structure factors, we find evidence for the emergence of the fully flippable and plaquette phases in the artificial quantum spin ice.
Self-adjointness criteria and self-adjoint extensions of the Laplace-Beltrami operator on $\alpha$-Grushin manifolds

The Grushin plane serves as one of the simplest examples of a sub-Riemannian manifold whose distribution is of non-constant rank. Despite the fact that the singular set where this distribution drops rank is itself a smoothly embedded submanifold, many basic results in the spectral theory of differential operators associated to this geometry remain open, with the question of characterizing self-adjoint extensions being a recent question of interest both in sub-Riemannian geometry and mathematical physics. In order to systematically address these questions, we introduce an exotic calculus of pseudodifferential operators adapted to the geometry of the singularity, closely related to the 0-calculus of Mazzeo arising in asymptotically hyperbolic geometry. Extending results of arXiv:2011.03300, arXiv:1105.4687, arXiv:1609.01724, this calculus allows us to give criterion for essential self-adjointness of the Curvature Laplacian, $\Delta-cS$ for $c>0$ (here $S$ is the scalar curvature). When this operator is not essentially self-adjoint, we determine several natural self-adjoint extensions. Our results generalize to a broad class of differential operators which are elliptic in this calculus.
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction

Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing deep learning methods suffer from significant performance degradation under complex real-world scenarios due to various factors, e.g., label scarcity and domain shift. In this paper, we propose a self-ensembling multi-graph neural network (SemiGNN-PPI) that can effectively predict PPIs while being both efficient and generalizable. In SemiGNN-PPI, we not only model the protein correlations but explore the label dependencies by constructing and processing multiple graphs from the perspectives of both features and labels in the graph learning process. We further marry GNN with Mean Teacher to effectively leverage unlabeled graph-structured PPI data for self-ensemble graph learning. We also design multiple graph consistency constraints to align the student and teacher graphs in the feature embedding space, enabling the student model to better learn from the teacher model by incorporating more relationships. Extensive experiments on PPI datasets of different scales with different evaluation settings demonstrate that SemiGNN-PPI outperforms state-of-the-art PPI prediction methods, particularly in challenging scenarios such as training with limited annotations and testing on unseen data.