Miracle-dz/UniFAR-FTUs huggingface dataset:
Effekte eines Beweglichkeits- und Krafttrainings auf die schmerzbedingten Beschwerden bei Brustkrebspatientinnen

Zusammenfassung Ziel Ziel dieser Studie war, Wirkungen eines Beweglichkeitstrainings an FIVE ® -Geräten in Kombination mit gerätegestützten Kraftübungen auf die schmerzbedingten Beschwerden bei Brustkrebspatientinnen in der Rehabilitation zu analysieren. Methoden Durchgeführt wurde eine Prätest-Posttest-Interventionsstudie mit Brustkrebspatientinnen (n=41), die während ihrer stationären Anschlussheilbehandlung in einer Rehabilitationsklinik in Bad Waldsee zwischen Februar und März 2020 in zwei Gruppen randomisiert wurden. Im Rahmen der medizinischen Trainingstherapie führte die Interventionsgruppe (n=24) ein Beweglichkeitstraining an den FIVE ® -Geräten in Kombination mit gerätegestütztem Krafttraining durch, während die Kontrollgruppe (n=17) ein gerätegestütztes Krafttraining absolvierte. Vor und nach der dreiwöchigen Intervention (3 Trainingseinheiten/Woche) wurden die Beweglichkeit der Schulter und die isokinetische Maximalkraft gemessen sowie die schmerzbedingten Beschwerden anhand einer selbstentwickelten Schmerzskala abgefragt. Ergebnisse Alle Patientinnen erreichten verbesserte Schmerzwerte mit Ausnahme der Kategorie 1 (Selbstversorgung/Körperpflege) in der Interventionsgruppe. Eine Prüfung der statistischen Unterschiede zwischen den Messzeitpunkten 1 und 2 ergab, dass bei der Kontrollgruppe keine statistisch signifikanten Verbesserungen bei der Schmerzwahrnehmung beobachtet werden können; demgegenüber zeigte die Experimentalgruppe zumindest beim Konstrukt „Arbeitsfähigkeit“ statistisch signifikante Verbesserungen. Die Berechnung der Effektgrößen r bzw. η 2 machte allerdings auch bei einigen statistisch nicht signifikanten Veränderungen deutlich, dass mittlere bis große Effekte hinsichtlich einer geringeren Schmerzwahrnehmung vorlagen. Schlussfolgerung Sowohl ein kombiniertes Kraft- und Beweglichkeitstraining an FIVE ® -Geräten als auch ein ausschließlich gerätegestütztes Krafttraining kann bei Mammakarzinom-Patientinnen in der Phase der Rehabilitation empfohlen werden.
The M/M/k with Deterministic Setup Times

Capacity management, whether it involves servers in a data center, or human staff in a call center, or doctors in a hospital, is largely about balancing a resource-delay tradeoff. On the one hand, one would like to turn off servers when not in use (or send home staff that are idle) to save on resources. On the other hand, one wants to avoid the considerable setup time required to turn an off server back on. This paper aims to understand the delay component of this tradeoff, namely, what is the effect of setup time on average delay in a multi-server system? Surprisingly little is known about the effect of setup times on delay. While there has been some work on studying the M/M/k with Exponentially-distributed setup times, these works provide only iterative methods for computing mean delay, giving little insight as to how delay is affected by k, by load, and by the setup time. Furthermore, setup time in practice is much better modeled by a Deterministic random variable, and, as this paper shows, the effect of a Deterministic setup time is nothing like that of an Exponentially-distributed setup time. This paper provides the first analysis of the M/M/k with Deterministic setup times. We prove a lower bound on the effect of setup on delay. Our result is a simple algebraic formula which provides insight into how delay scales with the input parameters. For more details, see the full paper.
Switching in ferroelectric polycrystals

The Rayleigh law and a propagating switching region of a ferroelectric polycrystal are studied with a finite element model. Each crystallite, represented by a cubic element in a finite element mesh, is a single domain that switches completely without a simulated domain wall motion. The model involves only electric field induced (i.e., ferroelectric) switching. Under a low applied field, the model reproduces the Rayleigh law. The model predicts that a switching crystallite and its follow-up switching crystallite are likely to be the nearest neighbors and that the switching sequence of the crystallites forms the shape of an ellipsoid the major axis of which is parallel with the applied field direction. A spontaneous polarization reverses by direct 180° reorientation if only one layer of the finite elements between two electrodes is simulated but reverses by two consecutive 90° domain reorientations if the number of stacked layers between the electrodes is equal to two or greater.
Three‐dimensional, 2.5‐minute, 7T phosphorus magnetic resonance spectroscopic imaging of the human heart using concentric rings

A 3D density-weighted concentric ring trajectory (CRT) MRSI sequence is implemented for cardiac 31P-MRS at 7T. The point-by-point k-space sampling of traditional phase-encoded CSI sequences severely restricts the minimum scan time at higher spatial resolutions. Our proposed CRT sequence implements a stack of concentric rings trajectory, with a variable number of rings and planes spaced to optimise the density of k-space weighting. This creates flexibility in acquisition time, allowing acquisitions substantially faster than traditional phase-encoded CSI sequences, while retaining high SNR. We first characterise the signal-to-noise ratio and point spread function of the CRT sequence in phantoms. We then evaluate it at five different acquisition times and spatial resolutions in the hearts of five healthy participants at 7T. These different sequence durations are compared with existing published 3D acquisition-weighted CSI sequences with matched acquisition times and spatial resolutions. To minimise the effect of noise on the short acquisitions, low-rank denoising of the spatio-temporal data was also performed after acquisition. The proposed sequence measures 3D localised PCr/ATP ratios of the human myocardium in 2.5 minutes, 2.6 times faster than the minimum scan time for the acquisition-weighted phase-encoded CSI. Alternatively, in the same scan time a 1.7-times smaller nominal voxel volume can be achieved. Low-rank denoising reduced the variance of measured PCr/ATP ratios by 11% across all protocols. The faster acquisitions permitted by 7T CRT 31P-MRSI could make cardiac stress protocols or creatine kinase rate measurements (which involve repeated scans) more tolerable for patients without sacrificing spatial resolution.
Next‐generation sequencing in breast pathology: real impact on routine practice over a decade since its introduction

The diagnosis, histomolecular classes of breast cancers (luminal A, luminal B, HER2‐enriched, and basal‐like), and accurate prediction of prognosis are commonly determined using morphological and phenotypical analyses in clinical practice worldwide. Therapeutic strategies are mostly based on the disease stage and molecular subclasses of breast cancer. Targeted therapies, such as anti‐HER2s, poly‐ADP ribose polymerase inhibitors or, to a lesser extent, phosphatidylinositol 3 kinase inhibitors, have substantially improved breast cancer patient prognosis over the past decades. Human epidermal growth factor receptor 2 (HER2) overexpression is widely determined based on immunohistochemistry, while next‐generation sequencing (NGS) is currently employed to assess the presence of molecular alterations, including breast cancer gene 1 (BRCA1) and 2 or phosphatidylinositol‐4,5‐bisphosphate 3‐kinase catalytic subunit alpha (PIK3CA) mutations, which are targets of these new approved therapies. In addition, next‐generation sequencing (NGS) can aid the pathologist in challenging situations, such as a diagnostic workup for a metastatic carcinoma in lymph nodes of unknown origin, differential diagnosis of spindle cell tumourtumor in the breast between metaplastic carcinoma, malignant PT and sarcoma, o, as well as determining relatedness between primary breast cancers and recurrences. NGS offers a powerful tool that enables the pathologist to combine morphological analyses together with molecular alterations in challenging diagnostic situations.
Estimation of Heat Transfer Coefficient of Cryogen Spray Cooling with Alifanov's Iterative Regularization Method

Cryogen spray cooling has been applied as an effective method to reduce skin damage during laser therapy for some vascular lesions. Applied over tens of milliseconds, the human skin can be instantaneously cooled for the subsequent application of laser therapy so that the skin will not be damaged by the laser heating. In this work, an inverse heat conduction problem is solved for the estimation of surface heat transfer coefficient of cryogen spray cooling based on internal temperature measurements. Alifanov's iterative regularization method is applied for the function estimation without any a priori information about the functional form of the heat transfer coefficient. The effects of experimental errors of temperature measurements and the number and position of the temperature sensors are investigated.
Effect of salt reduction interventions in lowering blood pressure: A comprehensive systematic review and meta-analysis of controlled clinical trials

Background Excessive salt intake results in hypertension (HTN), which is a major risk factor for cardiovascular disease (CVD). This review and meta-analysis aimed to evaluate the effect of salt reduction interventions on systolic blood pressure (SBP) and diastolic blood pressure (DBP). Methods Studies were identified via systematic searches of the databases, including PubMed, Embase, Scopus, and Web of Science. All the studies examining the effectiveness of salt reduction interventions on blood pressure (BP), regardless of age, sex, and HTN status, were included in the systematic review, and eligible studies were used in the meta-analysis. A random-effect model was applied for quantitative data synthesis. Results A total of 50 trials extracted from 40 articles (21 trials on nutrition education,10 on self-help materials,17 on salt substitutes, and 2 on food reformulation) were included in the systematic review. The pooled results of 44 eligible trials showed that salt substitution and nutrition education interventions had significant effects on both SBP (WMD: -7.44 mmHg, P<0.001 and WMD: -2.75 mmHg, P<0.001, respectively), and DBP (WMD: -3.77 mmHg, P<0.001 and WMD: -2.11 mmHg, P<0.001, respectively). Furthermore, using self-help materials led to a significant reduction in SBP among subjects aged 25–60 years (WMD: -2.60 mmHg, P = 0.008); it also decreased both SBP and DBP among those who were hypertensive (WMD: -3.87 mmHg, P = 0.003 and WMD: -2.91 mmHg, P<0.001, respectively). Conclusion Our results supported that salt substitution and nutrition education are effective nutrition strategies to lower BP. It seems that multi-component approaches could be more effective in improving BP status. However, further trials are required.
Understanding the neurodynamic process of decision-making for mobile application downloading

In this article, we try to explore and understand the neurodynamics of the decision-making process for mobile application downloading. We begin the model development in a rather unorthodox fashion. Patterns of brain activation regions are identified, across participants, at different time instance of the decision-making process. Region-wise activation knowledge from previous studies is used to put together the entire process model like a cognitive jigsaw puzzle. We find that there are indeed a common dynamic set of activation patterns that are consistent across people and apps. That is to say that not only are there consistent patterns of activation there is a consistent change from one pattern to another across time as people make the app adoption decision. Moreover, this pattern is clearly different for decisions that end in adoption than for decisions that end with no adoption.
Complete ranking procedures with appropriate loss functions

This paper treats the problem of comparing different evaluations of procedures which rank the variances of k normal populations. Procedures are evaluated on the basis of appropriate loss functions for a particular goal. The goal considered involves ranking the variances of k independent normal populations when the corresponding population means are unknown. The variances are ranked by selecting samples of size n from each population and using the sample variances to obtain the ranking. Our results extend those of various authors who looked at the narrower problem of evaluating the standard proceduv 3 associated with selecting the smallest of the population variances (see e.g.,P. Somerville (1975)). Different loss functions (both parametric and non-parametric) appropriate to the particular goal under consideration are proposed. Procedures are evaluated by the performance of their risk over a particular preference zone. The sample size n, the least favorable parametric configuration, and the maximum value o.
Overcoming the cytoplasmic retention of GDOWN1 modulates global transcription and facilitates stress adaptation

Dynamic regulation of transcription is crucial for the cellular responses to various environmental or developmental cues. Gdown1 is a ubiquitously expressed, RNA polymerase II (Pol II) interacting protein, essential for the embryonic development of metazoan. It tightly binds Pol II in vitro and competitively blocks the binding of TFIIF and possibly other transcriptional regulatory factors, yet its cellular functions and regulatory circuits remain unclear. Here, we show that human GDOWN1 strictly localizes in the cytoplasm of various types of somatic cells and exhibits a potent resistance to the imposed driving force for its nuclear localization. Combined with the genetic and microscope-based approaches, two types of the functionally coupled and evolutionally conserved localization regulatory motifs are identified, including the CRM1-dependent nucleus export signal (NES) and a novel Cytoplasmic Anchoring Signal (CAS) that mediates its retention outside of the nuclear pore complexes (NPC). Mutagenesis of CAS alleviates GDOWN1’s cytoplasmic retention, thus unlocks its nucleocytoplasmic shuttling properties, and the increased nuclear import and accumulation of GDOWN1 results in a drastic reduction of both Pol II and its associated global transcription levels. Importantly, the nuclear translocation of GDOWN1 occurs in response to the oxidative stresses, and the ablation of GDOWN1 significantly weakens the cellular tolerance. Collectively, our work uncovers the molecular basis of GDOWN1’s subcellular localization and a novel cellular strategy of modulating global transcription and stress-adaptation via controlling the nuclear translocation of GDOWN1.
Comparing T cell receptor repertoires using optimal transport

The complexity of entire T cell receptor (TCR) repertoires makes their comparison a difficult but important task. Current methods of TCR repertoire comparison can incur a high loss of distributional information by considering overly simplistic sequence- or repertoire-level characteristics. Optimal transport methods form a suitable approach for such comparison given some distance or metric between values in the sample space, with appealing theoretical and computational properties. In this paper we introduce a nonparametric approach to comparing empirical TCR repertoires that applies the Sinkhorn distance, a fast, contemporary optimal transport method, and a recently-created distance between TCRs called TCRdist. We show that our methods identify meaningful differences between samples from distinct TCR distributions for several case studies, and compete with more complicated methods despite minimal modeling assumptions and a simpler pipeline.
Superconducting properties of amorphous MoX (X=Si, Ge) alloy films for Abrikosov vortex memory

Structural and superconducting properties of amorphous MoSi (∼55%Si) and MoGe (∼30% Ge) films (200 and 50 nm in thickness) prepared by rf magnetron sputtering were examined for the purpose of application to Abrikosov vortex memory devices. Amorphous single‐phase films were obtained at concentrations of 20% Si and above, while a crystalline phase was detected even at a concentration of 30% Ge. The highest critical temperature Tc was 7.2–7.3 K in Mo‐20% Si films. Magnetic penetration depth λ at 4.2 K and upper critical field dependence on temperature Bc2(T) were measured. The Bc2(T) behavior in the vicinity of Tc showed a tail‐like shape, which most likely is the result of structural inhomogeneities in the prepared films. The λ dependence on Si concentration was satisfactorily explained through the Ginzburg–Landau–Abrikosov–Gorkov (GLAG) dirty limit theory using the corrected critical temperature T*c which is defined by the Bc2(T) results. Other superconducting parameters (GL parameter κGL and coherence len.
Strong identifiability and parameter learning in regression with heterogeneous response

Mixtures of regression are a powerful class of models for regression learning with respect to a highly uncertain and heterogeneous response variable of interest. In addition to being a rich predictive model for the response given some covariates, the parameters in this model class provide useful information about the heterogeneity in the data population, which is represented by the conditional distributions for the response given the covariates associated with a number of distinct but latent subpopulations. In this paper, we investigate conditions of strong identifiability, rates of convergence for conditional density and parameter estimation, and the Bayesian posterior contraction behavior arising in finite mixture of regression models, under exact-fitted and over-fitted settings and when the number of components is unknown. This theory is applicable to common choices of link functions and families of conditional distributions employed by practitioners. We provide simulation studies and data illustrations, which shed some light on the parameter learning behavior found in several popular regression mixture models reported in the literature.
Reinforcement Learning for Resilient Power Grids

Traditional power grid systems have become obsolete under more frequent and extreme natural disasters. Reinforcement learning (RL) has been a promising solution for resilience given its successful history of power grid control. However, most power grid simulators and RL interfaces do not support simulation of power grid under large-scale blackouts or when the network is divided into sub-networks. In this study, we proposed an updated power grid simulator built on Grid2Op, an existing simulator and RL interface, and experimented on limiting the action and observation spaces of Grid2Op. By testing with DDQN and SliceRDQN algorithms, we found that reduced action spaces significantly improve training performance and efficiency. In addition, we investigated a low-rank neural network regularization method for deep Q-learning, one of the most widely used RL algorithms, in this power grid control scenario. As a result, the experiment demonstrated that in the power grid simulation environment, adopting this method will significantly increase the performance of RL agents.
Convergence analysis of variable steps BDF2 method for the space fractional Cahn-Hilliard model

An implicit variable-step BDF2 scheme is established for solving the space fractional Cahn-Hilliard equation, involving the fractional Laplacian, derived from a gradient flow in the negative order Sobolev space $H^{-\alpha}$, $\alpha\in(0,1)$. The Fourier pseudo-spectral method is applied for the spatial approximation. The proposed scheme inherits the energy dissipation law in the form of the modified discrete energy under the sufficient restriction of the time-step ratios. The convergence of the fully discrete scheme is rigorously provided utilizing the newly proved discrete embedding type convolution inequality dealing with the fractional Laplacian. Besides, the mass conservation and the unique solvability are also theoretically guaranteed. Numerical experiments are carried out to show the accuracy and the energy dissipation both for various interface widths. In particular, the multiple-time-scale evolution of the solution is captured by an adaptive time-stepping strategy in the short-to-long time simulation.
Nonlinear regulation and path tracking of a wheeled mobile robot in polar coordinates

Abstract This paper presents regulation and path tracking control methods for wheeled mobile robots (WMRs) associated with kinematic models in two‐dimensional polar coordinates. The proposed two regulation control laws are designed via the Lyapunov indirect and direct stability theorems in order to asymptotically achieve nonlinear regulation. With the Lyapunov‐based stabilization law, a novel path tracking control method is proposed to achieve reference path following missions on a global scale except for an arbitrary small region around the origin; in particular, two special path tracking examples are analyzed with large initial tracking errors. Computer simulations and experimental results are described to confirm the efficacy of these proposed control approaches.
Low Variance Off-policy Evaluation with State-based Importance Sampling

In off-policy reinforcement learning, a behaviour policy performs exploratory interactions with the environment to obtain state-action-reward samples which are then used to learn a target policy that optimises the expected return. This leads to a problem of off-policy evaluation, where one needs to evaluate the target policy from samples collected by the often unrelated behaviour policy. Importance sampling is a traditional statistical technique that is often applied to off-policy evaluation. While importance sampling estimators are unbiased, their variance increases exponentially with the horizon of the decision process due to computing the importance weight as a product of action probability ratios, yielding estimates with low accuracy for domains involving long-term planning. This paper proposes state-based importance sampling, which drops the action probability ratios of sub-trajectories with ``negligible states'' -- roughly speaking, those for which the chosen actions have no impact on the return estimate -- from the computation of the importance weight. Theoretical results show this reduces the ordinary importance sampling variance from $O(\exp(H))$ to $O(\exp(X))$ where $X<H$ is the largest subtrajectory with non-negligible states. To identify negligible states, two search algorithms are proposed, one based on covariance testing and one based on state-action values. We formulate state-based variants of ordinary importance sampling, weighted importance sampling, per-decision importance sampling, incremental importance sampling, doubly robust off-policy evaluation, and stationary density ratio estimation. Experiments in four distinct domains show that state-based methods consistently yield reduced variance and improved accuracy compared to their traditional counterparts.