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Purpose: This study aimed to present a preliminary case analysis of the impact of regular and irregular exercise on autonomic regulation and cardiorespiratory performance in young women by comprehensively investigating the nocturnal heart rate variability (HRV) parameters. Methods: Two young female participants were monitored using noncontact ballistocardiography technology to assess their nocturnal HRV daily for 32 weeks. Participant 1 was a 28-year-old woman who engaged in regular running (approximately three times a week, 5km each time), and participant 2 was a 24-year-old woman who participated in irregular running (typically ≤3 times a week, 5km each time). Additionally, cardiorespiratory fitness was evaluated through maximal oxygen uptake (VO₂max), with running data and VO₂max measurements recorded using a wrist bracelet device. Results: During the experiment, the VO₂max value of participant 1 increased by 11.46%, whereas that of participant 2 increased by 3.42%. A correlation was observed between VO₂max and HRV, particularly in the high-frequency (HF) component. The correlation coefficient between ln HF and VO₂max of participant 1 was 0.64, whereas that of participant 2 was 0.28. Additionally, participant 1 exhibited lower HRV complexity than participant 2, with fuzzy entropy values for ln HF of 0.12 and 0.35, respectively. Conclusions: Long-term assessment revealed a correlation between VO₂max and nocturnal HRV in young female exercisers, particularly for the HF index. However, these findings may not apply to other populations, such as men or older individuals.

Chemogenomic experiments, where genetic and chemical perturbations are combined, provide data for discovering the relationships between genotype and phenotype. Traditionally, analysis of chemogenomic datasets has been done considering the sensitivity of the deletion strains to chemicals, and this has shed light on drug mechanism of action and detecting drug targets. Here, we computationally analyzed a large chemogenomic dataset, which combines more than 300 chemicals with virtually all gene deletion strains in the yeast S. cerevisiae. In addition to sensitivity relation between deletion strains and chemicals, we also considered the deletion strains that are resistant to chemicals. We found a small set of genes whose deletion makes the cell resistant to many chemicals. Curiously, these genes were enriched for functions related to RNA metabolism. Our approach allowed us to generate a network of drugs and genes that are connected with resistance or sensitivity relationships. As a quality assessment, we showed that the higher order motifs found in this network are consistent with biological expectations. Finally, we constructed a biologically relevant network projection pertaining to drug similarities, and analyzed this network projection in detail. We propose this drug similarity network as a useful tool for understanding drug mechanism of action.

The objective of the work is to investigate the classification of different movements based on the surface electromyogram (SEMG) pattern recognition method. The testing was conducted for four arm movements using several experiments with artificial neural network classification scheme. Six time domain features were extracted and consequently classification was implemented using back propagation neural classifier (BPNC). Further, the realization of projected network was verified using cross validation (CV) process; hence ANOVA algorithm was carried out. Performance of the network is analyzed by considering mean square error (MSE) value. A comparison was performed between the extracted features and back propagation network results reported in the literature. The concurrent result indicates the significance of proposed network with classification accuracy (CA) of 100% recorded from two channels, while analysis of variance technique helps in investigating the effectiveness of classified signal for recognition tasks.

This work is part of the analysis of the effects of constructional energy-saving measures to radon concentration levels in dwellings performed on behalf of the German Federal Office for Radiation Protection. In parallel to radon measurements for five buildings, both meteorological data outside the buildings and the indoor climate factors were recorded. In order to access effects of inhabited buildings, the amount of carbon dioxide (CO${}_2)$ was measured. For a statistical linear regression model, the data of one object was chosen as an example. Three dummy variables were extracted from the process of the CO₂ concentration to provide information on the usage and ventilation of the room. The analysis revealed a highly autoregressive model for the radon concentration with additional influence by the natural environmental factors. The autoregression implies a strong dependency on a radon source since it reflects a backward dependency in time. At this point of the investigation, it cannot be determined whether the influence by outside factors affects the source of radon or the habitant’s ventilation behavior resulting in variation of the occurring concentration levels. In any case, the regression analysis might provide further information that would help to distinguish these effects. In the next step, the influence factors will be weighted according to their impact on the concentration levels. This might lead to a model that enables the prediction of radon concentration levels based on the measurement of CO₂ in combination with environmental parameters, as well as the development of advices for ventilation.

One of the main goals of modern cosmology is to probe inflationary theories by looking on the imprint of primordial gravitational waves in the cosmic microwave background (CMB) polarization field. Future CMB experiments face the great challenge to search for this primordial B-mode signal. However, the CMB sky is also filled with secondary B-modes, including CMB lensing and astrophysical foregrounds. Extracting the CMB B-mode polarization from astrophysical contaminations is a primordial task towards detection of the primordial signal. We use the analytical method of blind separation (ABS) proposed by Zhang, P., et al. (2019) to reconstruct the CMB B-mode power spectrum in the presence of foregrounds and white noise considering a full sky analysis for r = 0.

In this paper, we summarize some of the main observational challenges for the standard Friedmann–Lemaître–Robertson–Walker (FLRW) cosmological model and describe how results recently presented in the parallel session “Large-scale Structure and Statistics” (DE3) at the “Fourteenth Marcel Grossman Meeting on General Relativity” are related to these challenges.

For about a decade, the baryon acoustic oscillation (BAO) peak at about 105h⁻¹ Mpc has provided a standard ruler test of the ΛCDM cosmological model, a member of the Friedmann–Lemaître–Robertson–Walker (FLRW) family of cosmological models— according to which comoving space is rigid. However, general relativity does not require comoving space to be rigid. During the virialisation epoch, when the most massive structures form by gravitational collapse, it should be expected that comoving space evolves inhomogeneous curvature as structure grows. The BAO peak standard ruler should also follow this inhomogeneous evolution if the comoving rigidity assumption is false. This “standard” ruler has now been detected to be flexible, as expected under general relativity.

The backreaction of inhomogeneities describes the effect of inhomogeneous structure on average properties of the Universe. We investigate this approach by testing the consistency of cosmological N-body simulations as non-linear structure evolves. Using the Delaunay Tessellation Field Estimator (DTFE), we calculate the kinematical backreaction Q from simulations on different scales in order to measure how much N-body simulations should be corrected for this effect. This is the first step towards creating fully relativistic and inhomogeneous N-body simulations. In this paper we compare the interpolation techniques available in DTFE and illustrate the statistical dependence of Q as a function of length scale.

To accomplish correct Bayesian inference from weak lensing shear data requires a complete statistical description of the data. The natural framework to do this is a Bayesian Hierarchical Model, which divides the chain of reasoning into component steps. Starting with a catalogue of shear estimates in tomographic bins, we build a model that allows us to sample simultaneously from the the underlying tomographic shear fields and the relevant power spectra (E-mode, B-mode, and EB, for auto- and cross-power spectra). The procedure deals easily with masked data and intrinsic alignments. Using Gibbs sampling and messenger fields, we show with simulated data that the large (over 67000-) dimensional parameter space can be efficiently sampled and the full joint posterior probability density function for the parameters can feasibly be obtained. The method correctly recovers the underlying shear fields and all of the power spectra, including at levels well below the shot noise.

The workshop focused on approaches to deduce changes in biological activity in cellular pathways and networks that drive phenotype from high-throughput data. Work in cancer has demonstrated conclusively that cancer etiology is driven not by single gene mutation or expression change, but by coordinated changes in multiple signaling pathways. These pathway changes involve different genes in different individuals, leading to the failure of gene-focused analysis to identify the full range of mutations or expression changes driving cancer development. There is also evidence that metabolic pathways rather than individual genes play the critical role in a number of metabolic diseases. Tools to look at pathways and networks are needed to improve our understanding of disease and to improve our ability to target therapeutics at appropriate points in these pathways.