Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and determinants of chronic respiratory diseases in adults in rural Sudan

BACKGROUND: Chronic respiratory diseases (CRDs) greatly contribute to worldwide mortality. Despite new data emerging from Africa, prevalence estimates and determinants of CRDs in rural settings are limited. This study sought to extend the existing research conducted in urban Sudan by conducting a rural comparison. METHODS: Participants aged ≥18 years (n = 1,850), living in rural Gezira State completed pre-and post-bronchodilator spirometry and a questionnaire. Prevalence of respiratory symptoms and spirometric abnormalities were reported. Regression analyses were used to identify risk factors for CRDs. RESULTS: Prevalence of chronic airflow obstruction (CAO) was 4.1% overall and 5.5% in those aged ≥40 years. Reversibility was seen in 6.4%. Low forced vital capacity (FVC) was seen in 58.5%, and at least one respiratory symptom was present in 40.7% of the participants. CAO was more common among people aged 60–69 years (OR 2.07, 95% CI 1.13–3.82) and less common among highly educated participants (OR 0.50, 95% CI 0.27–0.93). Being underweight was associated with lower FVC (OR 3.07, 95% CI 2.24–4.20). CONCLUSIONS: A substantial burden of CRD exists among adults in rural Sudan. Investment in CRD prevention and management strategies is needed.
Clinical study of left ventricular structure and function in patients with Anderson–Fabry disease before and after enzyme replacement therapy

Cardiac left ventricular hypertrophy (LVH) is the most common manifestation of heart involvement in Anderson–Fabry disease (AFD). Conventional cardiac imaging is not sensitive enough to detect early signs of LVH in AFD. It remains uncertain whether enzyme replacement therapy (ERT) can prevent LVH progression and improve myocardial function. This study aimed to assess the effectiveness of two‐dimensional speckle tracking echocardiography (2D‐STE) in early detection of cardiac involvement in AFD and monitoring the efficacy of agalsidase alfa and agalsidase beta therapy.
Self-disclosure by adolescents in therapy for eating difficulties: A Q-Methodology study

Facilitating client self-disclosure is essential to therapeutic effectiveness. Given the long-term consequences of eating difficulties for adolescents, there is a need for more research on self-disclosure in this group. This study investigated factors likely to influence adolescents’ decisions to self-disclose during psychological therapy for eating difficulties using Q-methodology. Participants (n = 28), recruited through child and adolescent mental health services in the UK, completed a task that involved sorting 47 statements to represent their viewpoint on self-disclosure. The 28 completed sorts were subjected to a by-participant factor analysis in order to identify distinct viewpoints in the sample. Three distinct factors were extracted. One factor emphasised the importance of therapist self-disclosure on decisions to disclose. In contrast, another factor placed more emphasis on the influence of eating disorder identity and readiness to change on disclosure decisions. The third factor placed emphasis on the quality of the therapeutic relationship and readiness to change as having most influence. Given the absence of a unifying factor representing what influences the decision to disclose, clinicians should ensure they explore with young people what might influence their decision to disclose.
The Therapeutic Synergy of Selinexor and Venetoclax in Mantle Cell Lymphoma Through Induction of DNA Damage and Perturbation of the DNA Damage Response

Introduction: Mantle cell lymphoma (MCL) can be stratified into blastoid and classical subtypes based on morphological features, with the former subtype having a poorer prognosis. Despite recent advances in targeted approaches, including multiple bruton tyrosine kinase inhibitors which yield impressive clinical responses and improve prognoses, MCL remains an incurable disease with frequent relapses. Additional therapeutic interventions are therefore unmet medical needs for the management of patients with MCL. Methods: Cell viability and apoptosis assays were employed to analyze the therapeutic interaction of venetoclax combined with selinexor in MCL cells. Western blot was used to investigate the potential mechanism of action for the synergy of venetoclax in combination with selinexor in MCL cells. Results: In this study, we revealed that both blastoid and classical MCL cells were vulnerable to the cytotoxic effects of selinexor, a well-established XPO1 inhibitor, manifested by loss of cell viability and induction of cell apoptosis. Moreover, our data indicated that the addition of venetoclax to selinexor showed synergistically decreased cell viabilities and increased cell deaths in blastoid and classical MCL cells compared to each single drug treatment. Either selinexor or venetoclax treatment alone decreased MCL1 expressions and increased BAX levels in MCL cells, and these effects were further enhanced by their combined regimen. Mechanistically, our findings demonstrated that induction of DNA damage and inactivation of DNA damage response were involved in the synergistic interaction of the drug combination regimen. Conclusion: Collectively, this study might provide a potential attractive therapy option for the treatment of MCL. However, the conclusion needs additional experimental validation in in vivo models and clinical evaluations are mandatory.
Speckle tracking tissue motion mitral annulus displacement to assess early changes in the left ventricle and its association with lung function in patients with chronic obstructive pulmonary disease

To evaluate the early changes in left ventricular (LV) in patients with chronic obstructive pulmonary disease (COPD) by measuring tissue motion mitral annulus displacement (TMAD) and three‐dimensional (3D) parameters using speckle tracking imaging (STI), and to explore its correlation with lung function.
Detection of Mooney faces is robust to image asymmetries produced by illumination

Face detection relies on the visual features that are shared across different faces. An important component of the basic spatial configuration of a face is symmetry around the vertical midline. Although human faces are structurally symmetrical, they can be asymmetrical in an image due to the direction of lighting or the position of the face. In the experiments presented here, we examined how face detection from simple contrast patterns that occur across the face is affected by the image asymmetries associated with variations in the horizontal lighting direction. We presented observers with two-tone images of faces (Mooney faces) that isolated the unique pattern of contrast in the shading and shadows on a face, illuminated from a wide range of horizontal directions. In two experiments, we found that face detection is surprisingly robust to these lighting changes, with sensitivity in discriminating between face and non-face patterns reduced only at the most extreme lighting directions. This tolerance to changes in the horizontal lighting direction depended partly on the orientation of the face, vertical lighting direction, and contrast polarity. Our results provide insight into how contrast cues produced by shading and shadows occurring across the facial surface are utilized by the visual system to detect human faces.
Landscape of gut microbiota and metabolites and their interaction in comorbid heart failure and depressive symptoms: a random forest analysis study

ABSTRACT Depression is an individual risk factor for poor prognosis in patients with heart failure (HF). Recent studies show that gut microbiota and metabolites play a critical role in comorbid HF and depressive symptoms. We recruited 95 subjects including 35 HF patients with depressive symptoms (HF-DS), 36 HF patients without depressive symptoms (HF-NDS), and 24 healthy controls (HC). The 16S rRNA, metagenome sequencing, and untargeted metabolomic analysis were employed to test fecal samples. Our analysis found a significant difference in composition of gut microbiota in HF-DS, HF-NDS, and HC populations. At the genus level, Mediterranea, Tolumona, and Parabacteroides were significantly increased in HF-DS patients compared with HF-NDS patients, while Pedobacter, Azospirillum, and Ruminiclostridium were significantly decreased. Furthermore, anti-inflammatory mediators (abietic acid, quinic acid, linoleic acid, etc.) and neurotransmitters (catechin, serotonin, tryptamine, phenylethylamine, etc.) were reduced in HF-DS. The enrichment analysis revealed that the gut microbiota highly conformed to the functional pathways of metabolites, and amino acid-related metabolism, fatty acid-related metabolism, and cAMP signaling pathways may be crucial biological mechanisms involved in the development of comorbid depression and HF. Finally, Cloacibacillus and alpha-tocopherol were determined as diagnostic markers for HF-DS patients. IMPORTANCE There is increasing evidence that alterations in gut microbial composition and function are associated with cardiovascular or psychiatric disease. Therefore, it is meaningful to investigate the taxonomic and functional characterization of the microbiota in HF patients who also have depressive symptoms. In this cross-sectional study, Cloacibacillus and alpha-tocopherol were determined as new diagnostic markers. Furthermore, intestinal microecosystem disorders are closely linked to depressive symptoms in HF patients, providing a new reference viewpoint for understanding the gut-heart/brain axis. There is increasing evidence that alterations in gut microbial composition and function are associated with cardiovascular or psychiatric disease. Therefore, it is meaningful to investigate the taxonomic and functional characterization of the microbiota in HF patients who also have depressive symptoms. In this cross-sectional study, Cloacibacillus and alpha-tocopherol were determined as new diagnostic markers. Furthermore, intestinal microecosystem disorders are closely linked to depressive symptoms in HF patients, providing a new reference viewpoint for understanding the gut-heart/brain axis.
Association between adverse childhood experience and unintended pregnancy among Japanese women: a large-scale cross-sectional study

Abstract Unintended pregnancy (UP) can negatively impact the health of mothers, children, and families. While Adverse Childhood Experiences (ACEs) are increasingly known to affect sexual health, the influence on pregnancy intention is not fully understood. This study examines the relationship between ACEs and UP and explores other related factors, using 5049 pregnant and postpartum women data from the Japan COVID-19 and Society Internet Survey (JACSIS). We measured participants’ pregnancy intentions, ACEs, family functioning, and social network size. Logistic regression analysis provided odds ratios and 95% confidence intervals (CI). The prevalence of UP was approximately 16.5% (n = 893). Cumulative ACEs were consistently associated with UP, even after adjusting for intermediate variables in adulthood. The odds ratio for UP with a single ACE was 1.00 (CI: 0.82–1.21) but rose significantly with multiple ACEs: 1.39 (CI: 1.10–1.76) with double, 1.38 (CI: 1.02–2.86) with triple, and 1.81 (CI: 1.37–2.39) with more. Additionally, bad family functioning and lack of social networks emerged as contributors to UP. In conclusion, this study showed that ACEs are potentially correlated with UP. A deeper understanding of the transition from childhood experiences to UP is important for health interventions, necessitating further investigation.
Altermagnetism in the hopping regime

Conventional antiferromagnets are known for their time-reversal symmetry in their electronic structure, which results in a zero anomalous Hall coefficient. On the other hand, compensated magnets with noncollinear or canted moments or altermagnets can yield a nonzero anomalous Hall signal and a nondissipative transversal current. While high-symmetry systems typically exhibit an isotropic Hall effect, more interesting are low-symmetry systems, such as hematite, which demonstrates exceptional magnetotransport behavior as it becomes conductive upon slight Ti doping. We scrutinize the magnetotransport in Titanium-doped hematite, revealing a pronounced and unconventional symmetry dependence, particularly contingent on crystal orientation. Our findings establish a compelling correlation between our experimental observations and the classification of hematite as an altermagnet with anisotropic magnetotransport and anomalous Hall effect. Remarkably, our observations result from measurements in the hopping-transport regime, showing that particular transport properties in altermagnets are not limited to conventional band conduction.
Resolving Horizon-Scale Dynamics of Sagittarius A*

Sagittarius A* (Sgr A*), the supermassive black hole at the heart of our galaxy, provides unique opportunities to study black hole accretion, jet formation, and gravitational physics. The rapid structural changes in Sgr A*'s emission pose a significant challenge for traditional imaging techniques. We present dynamic reconstructions of Sgr A* using Event Horizon Telescope (EHT) data from April 6th and 7th, 2017, analyzed with a one-minute temporal resolution with the Resolve framework. This Bayesian approach employs adaptive Gaussian Processes and Variational Inference for data-driven self-regularization. Our results not only fully confirm the initial findings by the EHT Collaboration for a time-averaged source but also reveal intricate details about the temporal dynamics within the black hole environment. We find an intriguing dynamic feature on April 6th that propagates in a clock-wise direction. Geometric modelling with ray-tracing, although not fully conclusive, indicates compatibility with high-inclination configurations of about $\theta_o = 160^\circ$, as seen in other studies.
Noise-Free Score Distillation

Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains. In this paper, we reexamine the SDS process and introduce a straightforward interpretation that demystifies the necessity for large Classifier-Free Guidance (CFG) scales, rooted in the distillation of an undesired noise term. Building upon our interpretation, we propose a novel Noise-Free Score Distillation (NFSD) process, which requires minimal modifications to the original SDS framework. Through this streamlined design, we achieve more effective distillation of pre-trained text-to-image diffusion models while using a nominal CFG scale. This strategic choice allows us to prevent the over-smoothing of results, ensuring that the generated data is both realistic and complies with the desired prompt. To demonstrate the efficacy of NFSD, we provide qualitative examples that compare NFSD and SDS, as well as several other methods.
Gonality of curves whose normalizations are one or two copies of $\mathbb P^1$

We study the gonality of curves $C$ over $\mathbb C$ whose normalization is composed of one or two copies of $\mathbb P^1$. In the first case, $C$ is a nodal curve with $g(C)$ nodes, and in the second case $C$ is a so-called binary curve. In any case we show that the usual bound $\mathrm{gon}(C)\leq\lfloor\frac{g(C)+3}{2}\rfloor$ holds if $g(C)\geq 2$, with equality holding generically.
Comprehensive Bayesian analysis of FRB-like bursts from SGR 1935+2154 observed by CHIME/FRB

The bright millisecond-duration radio burst from the Galactic magnetar SGR 1935+2154 in 2020 April was a landmark event, demonstrating that at least some fast radio burst (FRB) sources could be magnetars. The two-component burst was temporally coincident with peaks observed within a contemporaneous short X-ray burst envelope, marking the first instance where FRB-like bursts were observed to coincide with X-ray counterparts. In this study, we detail five new radio burst detections from SGR 1935+2154, observed by the CHIME/FRB instrument between October 2020 and December 2022. We develop a fast and efficient Bayesian inference pipeline that incorporates state-of-the-art Markov chain Monte Carlo techniques and use it to model the intensity data of these bursts under a flexible burst model. We revisit the 2020 April burst and corroborate that both the radio sub-components lead the corresponding peaks in their high-energy counterparts. For a burst observed in 2022 October, we find that our estimated radio pulse arrival time is contemporaneous with a short X-ray burst detected by GECAM and HEBS, and Konus-Wind and is consistent with the arrival time of a radio burst detected by GBT. We present flux and fluence estimates for all five bursts, employing an improved estimator for bursts detected in the side-lobes. We also present upper limits on radio emission for X-ray emission sources which were within CHIME/FRB's field-of-view at trigger time. Finally, we present our exposure and sensitivity analysis and estimate the Poisson rate for FRB-like events from SGR 1935+2154 to be $0.005^{+0.082}_{-0.004}$ events/day above a fluence of $10~\mathrm{kJy~ms}$ during the interval from 28 August 2018 to 1 December 2022, although we note this was measured during a time of great X-ray activity from the source.
The Word2vec Graph Model for Author Attribution and Genre Detection in Literary Analysis

Analyzing the writing styles of authors and articles is a key to supporting various literary analyses such as author attribution and genre detection. Over the years, rich sets of features that include stylometry, bag-of-words, n-grams have been widely used to perform such analysis. However, the effectiveness of these features largely depends on the linguistic aspects of a particular language and datasets specific characteristics. Consequently, techniques based on these feature sets cannot give desired results across domains. In this paper, we propose a novel Word2vec graph based modeling of a document that can rightly capture both context and style of the document. By using these Word2vec graph based features, we perform classification to perform author attribution and genre detection tasks. Our detailed experimental study with a comprehensive set of literary writings shows the effectiveness of this method over traditional feature based approaches. Our code and data are publicly available at https://cutt.ly/svLjSgk
Three Pillars improving Vision Foundation Model Distillation for Lidar

Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervision. Ideally, 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results, obtained thanks to distillation methods that keep improving. Yet, we still notice a large performance gap when measuring the quality of distilled and fully supervised features by linear probing. In this work, instead of focusing only on the distillation method, we study the effect of three pillars for distillation: the 3D backbone, the pretrained 2D backbones, and the pretraining dataset. In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features, and to improve the robustness of the pretrained backbones to domain gaps and perturbations.
Direct numerical analysis of dynamic facilitation in glass-forming liquids

We propose a computational strategy to quantify the temperature evolution of the timescales and lengthscales over which dynamic facilitation affects the relaxation dynamics of glass-forming liquids at low temperatures, that requires no assumption about the nature of the dynamics. In two glass models, we find that dynamic facilitation depends strongly on temperature, leading to a subdiffusive spreading of relaxation events which we characterize using a temperature-dependent dynamic exponent. We also establish that this temperature evolution represents a major contribution to the increase of the structural relaxation time.
Local Coordination Modulates the Reflectivity of Liquefied Si–Ge Alloys

The properties of liquid Si-Ge binary systems at melting conditions deviate from those expected by the ideal alloy approximation. Particularly, a non-linear dependence of the dielectric functions occurs with the reflectivity of liquid Si-Ge reaching a maximum at 50\% Ge content, being 10\% higher than in pure Si or Ge. Using \textit{ab initio} methodologies, we modelled liquefied Si-Ge alloys, unveiling very high coordination numbers and poor symmetry in the first coordination shell with respect to Si and Ge, related to different bonding properties. We simulated optical functions, quantitatively replicating the aforementioned reflectivity trend and we highlighted a direct relationship between atomic structure and optical properties, indicating that the unusual optics arises from Si-Ge higher local coordination characterized by low symmetry. We forecast further implications for the overall class of these alloys. These findings expand our comprehension of liquefied semiconductors and are essential for implementing controlled laser melting procedures to highly dope these materials for advanced transistors, superconductors, sensors and plasmonic devices.
Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting

Accurately forecasting solar power generation is crucial in the global progression towards sustainable energy systems. In this study, we conduct a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. Our controlled experiments reveal promising advantages of QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical findings demonstrate QLSTM's potential to swiftly assimilate complex time series relationships, enabled by quantum phenomena like superposition. However, realizing QLSTM's full capabilities necessitates further research into model validation across diverse conditions, systematic hyperparameter optimization, hardware noise resilience, and applications to correlated renewable forecasting problems. With continued progress, quantum machine learning can offer a paradigm shift in renewable energy time series prediction. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM, while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study elucidates a promising trajectory for quantum learning in renewable forecasting. Additional research and development can further actualize this potential to achieve unprecedented accuracy and reliability in predicting solar power generation worldwide.