Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Associated with Increased Analgesic Use in German Women with Endometriosis during the COVID-19 Pandemic

(1) Background: Endometriosis is a frequent chronic pain condition in women of fertile age. Pain management with analgesics is frequently used by women with endometriosis. During the COVID-19 pandemic, access to health services was temporarily restricted in various countries for persons without serious conditions, resulting in increased physical and mental health issues. The present study was conducted in order to assess the risk factors predicting increased analgesic intake by women with endometriosis during the COVID-19 pandemic. (2) Methods: The increased intake of over-the-counter (OTC) and prescription-only (PO) analgesics was assessed with an anonymous online questionnaire, along with demographic, pandemic-specific, disease-specific, and mental health characteristics. Anxiety and depression were assessed with the Generalized Anxiety Disorder Scale (GAD-2) and the Patient Health Questionnaire for Depression (PHQ-2), respectively. Pain-induced disability was assessed with the pain-induced disability index (PDI). (3) Results: A high educational level (OR 2.719; 95% CI 1.137–6.501; p = 0.025) and being at higher risk for depressive disorders, as measured by PHQ-2 ≥ 3 (OR 2.398; 95% CI 1.055–5.450; p = 0.037), were independent risk factors for an increased intake of OTC analgesics. Current global pain-induced disability (OR 1.030; 95% CI 1.007–1.054; p = 0.010) was identified as a risk factor for an increased intake of PO pain medication. The degree of reduction in social support and in social networks were independent predictors of an increased intake of PO analgesics in a univariate logistic regression analysis, but lost significance when adjusted for additional possible influencing factors. (4) Conclusions: In this population, an increased intake of OTC analgesics was related to a higher educational level and having a depressive disorder, while a higher pain-induced disability was an independent risk factor for an increased intake of PO analgesics. Pandemic-specific factors did not significantly and independently influence an increased intake of analgesics in women with endometriosis during the first wave of the COVID-19 pandemic in Germany. Healthcare providers should be aware of the possible factors related to increased analgesic use in women with endometriosis in order to identify persons at risk for the misuse of pain medication and to prevent potential adverse effects.
Potassium Channels as Therapeutic Targets in Pulmonary Arterial Hypertension

Pulmonary arterial hypertension (PAH) is a devastating disease with high morbidity and mortality. Deleterious remodeling in the pulmonary arterial system leads to irreversible arterial constriction and elevated pulmonary arterial pressures, right heart failure, and eventually death. The difficulty in treating PAH stems in part from the complex nature of disease pathogenesis, with several signaling compounds known to be involved (e.g., endothelin-1, prostacyclins) which are indeed targets of PAH therapy. Over the last decade, potassium channelopathies were established as novel causes of PAH. More specifically, loss-of-function mutations in the KCNK3 gene that encodes the two-pore-domain potassium channel KCNK3 (or TASK-1) and loss-of-function mutations in the ABCC8 gene that encodes a key subunit, SUR1, of the ATP-sensitive potassium channel (KATP) were established as the first two potassium channelopathies in human cohorts with pulmonary arterial hypertension. Moreover, voltage-gated potassium channels (Kv) represent a third family of potassium channels with genetic changes observed in association with PAH. While other ion channel genes have since been reported in association with PAH, this review focuses on KCNK3, KATP, and Kv potassium channels as promising therapeutic targets in PAH, with recent experimental pharmacologic discoveries significantly advancing the field.
On the geomagnetic effects of solar wind interplanetary magnetic structures

We present in this work a statistical study of the geoeffectiveness of the solar wind magnetic interplanetary structures over the entire observational period (1964–2003). The structures studied were magnetic clouds (MCs, 170 events), corotating interaction regions (CIRs, 727 events) and interplanetary shocks (830 events). The geoeffectiveness was assessed in terms of the geomagnetic index Kp, AE, and Dst peak values within 2 days after the interplanetary structure had passed near Earth's orbit. Frequency distributions were obtained that give the probability of every interplanetary structure being followed by intense, moderate, weak, or quiet (none) magnetic activity levels. The knowledge of probability distribution is important in schemes to forecast space weather conditions after the detection, by in situ solar wind observations, of an interplanetary structure approaching Earth. We observed that magnetic clouds are more efficient than shocks or CIRs in producing all the geomagnetic activity disturbances; CIRs are themselves more geoeffective as measured by the AE activity. We have confirmed that compound structures (shocks plus MCs) are more geoeffective in every type of magnetospheric activity than isolated structures.
Modélisation bayésienne hiérarchique pour l'estimation de matrice de covariance - Application à la gestion actif-passif de portefeuilles financiers

Ce papier concerne l'estimation de matrices de covariance dans le cas ou le nombre de donnees utilisees pour l'estimation est faible par rapport a la dimension du probleme et ou les methodes d'estimation classiques fondees sur le Maximum de Vraisemblance sont peu robustes. Nous proposons une methode d'estimation non supervisee fondee sur une modelisation bayesienne hierarchique du probleme d'estimation de matrice de covariance : on pose une loi Inverse Wishart a priori pour la matrice, conditionnellement aux hyperparametres sur lesquels on pose des a priori de reference. On considere une matrice cible de structure diagonale. L'estimateur bayesien associe au cout entropique sera approche par des methodes de type Monte Carlo par Chaines de Markov. On comparera empiriquement notre estimateur avec celui obtenu par Maximum de Vraisemblance. L'aspect regularisant de la methode sera etudie en l'appliquant sur donnees financieres dans un cadre de gestion actif-passif, ou le nombre de donnees est faible par rapport a la taille de la matrice.
Beskytter tannpasta mot erosjoner?

Forfattere Aida Mulic, postdoktor. Avdeling for kariologi og gerodontologi, Institutt for klinisk odontologi, Universitetet i Oslo Anne Bjørg Tveit, professor. Avdeling for kariologi og gerodontologi, Institutt for klinisk odontologi, Universitetet i Oslo Ingerid Marie Bratli Delbeck, Master odont. 2016, Universitetet i Oslo Ida Kristine Lie, Master odont. , 2016, Universitetet i Oslo Kjersti R. Stenhagen, førsteamanuensis, Avdeling for kariologi og gerodontologi, Institutt for klinisk odontologi, Universitetet i Oslo Det er vitenskapelig dokumentert at konvensjonelle tannpastaer gir noe beskyttelse mot erosjoner. Samtidig oppstår erosjoner, til tross for at de fleste pusser tennene 1–2 ganger daglig med fluortannkrem. Det tyder på at noen personer har behov for mer beskyttelse enn andre, via modifisering av risikofaktorer og mulig bruk av beskyttende produkter. Det er velkjent at noen enkeltpersoner eller grupper har høyere risiko for å utvikle erosjoner enn andre. Samtidig er det vist at noen individer ikke utvikler erosjoner til tross for massiv syrepåvirkning på tennene. Nyere studier indikerer at dette kan være genetisk bestemt. Konvensjonelle tannpastaer er en del av den daglige munnhygienen, men basert på litteraturen anses de å ha begrenset og usikker forebyggende effekt mot erosjoner. På en konsensuskonferanse i Bern 2015 ble det konkludert at produkter med tinnfluorid og tinnklorid har potensial for å redusere progresjonen av erosjoner. Når det gjelder produkter med andre tilsetningsstoffer, så mangler det dokumentasjon av effekten.
Reluctivity characteristics in nonlinear finite element analysis of magnetostatic fields

In the finite element analysis of nonlinear magnetic materials, it is commonly convenient [1] to model reluctivity ν, the inverse of permeability µ, as k_{1} \exp (k_{2}B^{2}) + k_{3} . The constants k_{1}, k_{2} and k 3 are derived by trial and error [1]. We communicate a faster and more accurate way of computing these constants systematically, by applying the Newton process to the equations for the constants. Experience with this and the other standard reluctivity curve model using spline fits [2], in finite element analysis, is also commented on.
Healthy Lifestyle, Genetic Risk and Brain Health: A Gene-Environment Interaction Study in the UK Biobank

Genetic susceptibility and lifestyle affect the risk of dementia but there is little direct evidence for their associations with preclinical changes in brain structure. We investigated the association of genetic dementia risk and healthy lifestyle with brain morphometry, and whether effects from elevated genetic risk are modified by lifestyle changes. We used prospective data from up to 25,894 UK Biobank participants (median follow-up of 8.8 years), and defined healthy lifestyle according to American Heart Association criteria as BMI < 30, no smoking, healthy diet and regular physical activity). Higher genetic risk was associated with lower hippocampal volume (beta −0.16 cm3, 95% CI −0.22, −0.11) and total brain volume (−4.34 cm3, 95% CI −7.68, −1.01) in participants aged ≥60 years but not <60 years. Healthy lifestyle was associated with higher total brain, grey matter and hippocampal volumes, and lower volume of white matter hyperintensities, with no effect modification by age or genetic risk. In conclusion, adverse effects of high genetic risk on brain health were only found in older participants, while adhering to healthy lifestyle recommendations is beneficial regardless of age or genetic risk.
DETERMINING NODE DENSITY TO GUARANTEE AVERAGE LATENCY IN DUTY-CYCLED WIRELESS SENSOR NETWORKS

Some applications of Internet of Things (IoTs) require that generated data are delivered to the sink within an end-to-end (E2E) delay bound. Moreover, to save energy consumption or to lengthen the network lifetime, a duty cycle scheme is applied in which sensor nodes periodically switch the radio on and off. In the duty-cycled networks, packet delay is greatly affected by node deployment, therefore we aim to determine node density in a wireless sensor network in order to guarantee the average E2E delay given network parameters using the probability theory. Simulation results show that the proposed node deployment method consumes less energy while requiring a smaller number of sensor nodes than those of other existing works.
Robust and Explainable Fault Diagnosis With Power-Perturbation-Based Decision Boundary Analysis of Deep Learning Models

Robustness of neural network models is important in fault diagnosis (FD) because uncertainty in operating conditions varies the power spectral densities of vibration data; however, it is unknown to users due to the limited explainability of the models. This article proposes an FD framework with a power-perturbation-based decision boundary analysis (POBA) to explain the decision boundaries of vibration classification models. In the POBA, perturbed data are obtained from training data by power perturbation on frequency bands centering on dominant class-discriminative frequencies. The decision boundary of a model is then evaluated and visualized to users by testing the model on the perturbed data. Furthermore, the decision boundary information can be used to define a robustness score per class, and a robust model can be obtained by ensembling trained models using their robustness score per class. Demonstration using two vibration datasets verifies the explainability and robustness of the proposed FD framework.
Motion Analysis and Real‐Time Trajectory Prediction of Magnetically Steerable Catalytic Janus Micromotors

Chemically driven micromotors display unpredictable trajectories due to the rotational Brownian motion interacting with the surrounding fluid molecules. This hampers the practical applications of these tiny robots, particularly where precise control is a requisite. To overcome the rotational Brownian motion and increase motion directionality, robots are often decorated with a magnetic composition and guided by an external magnetic field. However, despite the straightforward method, explicit analysis and modeling of their motion have been limited. Here, catalytic Janus micromotors are fabricated with distinct magnetizations and a controlled self‐propelled motion with magnetic steering is shown. To analyze their dynamic behavior, a dynamic model that can successfully predict the trajectory of micromotors in uniform viscous flows in real time by incorporating a form of state‐dependent‐coefficient with a robust two‐stage Kalman filter is theoretically developed. A good agreement is observed between the theoretically predicted dynamics and experimental observations over a wide range of model parameter variations. The developed model can be universally adopted to various designs of catalytic micro‐/nanomotors with different sizes, geometries, and materials, even in diverse fuel solutions. Finally, the proposed model can be used as a platform for biosensing, detecting fuel concentration, or determining small‐scale motors’ propulsion mechanisms in an unknown environment.
An obstruction to the conformal compactification of Riemannian manifolds

In this paper, we study noncompact complete Riemannian nmanifolds with n > 3 which are not pointwise conformal to subdomains of any compact Riemannian n-manifold. For this, we compare the Sobolev Quotient at infinity of a noncompact complete Riemannian manifold with that of the singular set in a compact Riemannian manifold using the method for the Yamabe problem.
View-invariant, partially occluded human detection in still images using part bases and random forest

Abstract. This paper presents a part-based human detection method that is invariant to variations in the view of the human and partial occlusion by other objects. First, to address the view variance, parts are extracted from three views: frontal-rear, left profile, and right profile. Then a random set of rectangular parts are extracted from the upper, middle, and lower body as the distribution of Gaussian. Second, an individual part classifier is constructed using random forests across all parts extracted from the three views. From the part locations of each view, part vectors (PVs) are generated and part bases (PB) are also formalized by clustering PVs with their weights of each PB. For testing, a PV for the frontal-rear view is estimated using trained part detectors and is then applied to the trained PB for each view class. Then the distance is computed between the PB and PVs. After applying the same process to the other two views, the final human and its view having the minimum score are selected. The proposed method is applied to pedestrian datasets and its detection precision is, on average, 0.14 higher than related methods, while achieving a faster or comparable processing time with an average of 1.85 s per image.
Seroprevalence of Anti-tTg-IgA among Symptomized Celiac Disease Patients and Their Correlation with Rotavirus Infection

Celiac disease (CD) is a chronic inflammatory disorder in the intestinal tract as a response to the use of gluten in genetically predisposed individuals. It is a worldwide problem, with a high prevalence rate in North America. This is a descriptive cross-sectional study involving 1090 samples collected from different hospitals of Rawalpindi and Islamabad, Pakistan, from January 2019 to December 2019. In this study, 1090 blood samples screened for seroprevalence of anti-tTG antibodies in CD symptomatic patients via ELISA (enzyme-linked immunosorbent assay). 1090 fecal samples from the same CD patients were collected and tested for the presence of rotavirus (RV) via ELISA and RT-PCR. Of the 1090 patients tested for seroprevalence of anti-tTG antibodies, 112/1090 (10.3%) were found to be positive. Of the 112 anti-tTG-positive patients, 78/112 (70%) were positive for RV via ELISA and 74/112 (66.1%) were RV positive via RT-PCR. A statistically significant association was reported between rotavirus infection and celiac disease (p˂0.05). Anti-tTG antibodies were higher in age group 6 (12-18 years) patients (18.4%) and at minimum in age group 3 (1-3 years) patients (4.8%). However, there was a statistically insignificant relationship between group age and CD prevalence (p > 0.05). The highest CD prevalence was noted during winter season (19.6%) and the lowest (3.0%) during fall/autumn. Our study findings demonstrate that Pakistan has a high prevalence of CD compared to other studies. Further studies in the fields of environmental risk factors and treatment with more advanced serological and histopathological studies are needed in the future.
D-term chaotic inflation in supergravity and leptogenesis

We present a new model of D-term dominated chaotic inflation in supergravity. The F-flat direction present in this model is lifted by the dominant D-term, which leads to chaotic inflation and subsequent reheating. No cosmic string is formed after inflation because the U(1) gauge symmetry is broken during inflation. The leptogenesis scenario via the inflaton decay in our D-term chaotic inflation scenario is also discussed.
Does Government Debt Matter for Economic Growth? A Critical Literature Review with Special Focus to Sri Lanka

At present, continuous accumulation of government debt and its consequences to the economy become a vibrant and debatable topic in Sri Lanka. Analysts and policy makers are divided in their opinion with regard to the economic consequences on the debt incurred by the Sri Lankan government. This study discusses the existing theoretical and empirical literature on the relationship between public debt and economic growth. Initially, the study reviews the theories related to public debt and economic growth nexus and thereafter proceed to survey the recent empirical literature available on the above. It has been observed that the existing theoretical literature on debt and growth nexus suggest highly heterogeneous conclusions despite the negative relationship which exist between public debt and economic growth and are widely accepted by economists. Subsequent to the recent empirical findings of inverted nonlinear relationship between public debt and economic growth, the economists widely focus to find out the optimal debt levels of countries. The study shows an inverted U-shaped nonlinear relationship between public debt and economic growth in Sri Lanka which is between the threshold of 80 per cent and 90 per cent. In addition to the above, the study finds that there is a considerable gap existing among available literature that does not address the channels through which public debt affect economic growth in a detailed manner.
sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants. This review exposes the current scenario of the movement classification system, providing valuable information for new researchers and guiding future works towards myo-controlled devices.
Totally Geodesic Orbits of the Isotropy Subgroups of Compact Riemannian Symmetric Spaces

Riemannian symmetric spaces and their submanifolds produce abundant ex,amples for Riemannian geometry. In this paper we investigate totally geodesic orbits of the isotropy subgroups of irreducible compact Riemannian symmetric spaces and calculate their dimens'ions in some simple cases. Let U/K be an irreducible Riemannian symmetric space of compact type, u be the Lie algebra of U and{be the Lie algebra of K Then we have a Cartandecompositionofu: , u =t+p,
A Negative Answer to a Problem of Aldous on Determination of Exchangeable Sequences

We present results concerning when the joint distribution of an exchangeable sequence is determined by the marginal distributions of its partial sums. The question of whether or not this determination occurs was posed by David Aldous. We then consider related uniqueness problems, including a continuous time analog to the Aldous problem and a randomized univariate moment problem.