Regular Strength Research Articles

Constrained Plug-and-Play Priors for Image Restoration.

The Plug-and-Play framework has demonstrated that a denoiser can implicitly serve as the image prior for model-based methods for solving various inverse problems such as image restoration tasks. This characteristic enables the integration of the flexibility of model-based methods with the effectiveness of learning-based denoisers. However, the regularization strength induced by denoisers in the traditional Plug-and-Play framework lacks a physical interpretation, necessitating demanding parameter tuning. This paper addresses this issue by introducing the Constrained Plug-and-Play (CPnP) method, which reformulates the traditional PnP as a constrained optimization problem. In this formulation, the regularization parameter directly corresponds to the amount of noise in the measurements. The solution to the constrained problem is obtained through the design of an efficient method based on the Alternating Direction Method of Multipliers (ADMM). Our experiments demonstrate that CPnP outperforms competing methods in terms of stability and robustness while also achieving competitive performance for image quality.

Disentangling Magnetic Fields in NGC 6946 with Wide-band Polarimetry

We present λ13 cm polarization observations of the nearby spiral galaxy NGC 6946 with the Westerbork Synthesis Radio Telescope (WSRT) to examine the nearside halo magnetic fields. Despite λ13 cm exhibiting similar two-dimensional morphology as observed at longer (λ18–22 cm) or shorter (λ3 and λ6 cm) wavelengths, more complete frequency coverage will be required to explain the gap in polarization in the southwest quadrant of the galaxy. We fit models of the turbulent and coherent line-of-sight magnetic fields to the fractional degree of linearly polarized emission at λ3, λ6, λ13, λ18, and λ22 cm from observations taken with the WSRT, Karl G. Jansky Very Large Array, and Effelsberg telescopes. The results favor a multilayer turbulent magneto-ionized medium consistent with current observations of edge-on galaxies. We constrain the physical properties of the synchrotron-emitting thin and thick disks (scale heights of 300 pc and 1.4 kpc, respectively) along with the thermal thick disk and halo (scale heights of 1 and 5 kpc, respectively). Our preferred model indicates a clumpy and highly turbulent medium within 1 kpc of the midplane, and a diffuse extraplanar layer with a substantially lower degree of Faraday depolarization. In the halo, we estimate a regular magnetic field strength of 0.4–2.2 μG and that turbulence and a total magnetic field strength of ∼6 μG result in a Faraday dispersion of σ RM = 4–48 rad m−2. This work is an example of how the advanced capabilities of modern radio telescopes are opening a new frontier for the study of cosmic magnetism.

Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation

This article advocates proximal Markov chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Bayesian imaging literature, ProxMCMC employs the Moreau-Yosida envelope for a smooth approximation of the total-variation regularization term, fixes variance and regularization strength parameters as constants, and uses the Langevin algorithm for the posterior sampling. We extend ProxMCMC to be fully Bayesian by providing data-adaptive estimation of all parameters including the regularization strength parameter. More powerful sampling algorithms such as Hamiltonian Monte Carlo are employed to scale ProxMCMC to high-dimensional problems. Analogous to the proximal algorithms in optimization, ProxMCMC offers a versatile and modularized procedure for conducting statistical inference on constrained and regularized problems. The power of ProxMCMC is illustrated on various statistical estimation and machine learning tasks, the inference of which is traditionally considered difficult from both frequentist and Bayesian perspectives.

High-Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data

Similarly to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult, due to privacy concerns. As a result, conditions like rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods of computer-aided diagnosis. In recent years, generative adversarial networks (GANs), mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images. In this study, for the first time, a small dataset of rosacea with 300 full-face images was utilized to further investigate the possibility of generating synthetic data. Our experimentation demonstrated that the strength of R1 regularization is crucial for generating high-fidelity rosacea images using a few hundred images. This was complemented by various experimental settings to ensure model convergence. We successfully generated 300 high-quality synthetic images, significantly contributing to the limited pool of rosacea images for computer-aided diagnosis. Additionally, our qualitative evaluations by 3 expert dermatologists and 23 non-specialists highlighted the realistic portrayal of rosacea features in the synthetic images. We also provide a critical analysis of the quantitative evaluations and discuss the limitations of solely relying on validation metrics in the field of computer-aided clinical image diagnosis.

Korean Adolescents before and after COVID-19: Changes in Physical Activity, Mental Health, and Hygiene Management.

Adolescence is when health behaviors, namely, physical activity (PA), mental health, and hygiene management, are established. As the young represent our future societal welfare, their health requires attention, particularly in the face of a pandemic. We explored changes in Korean adolescent PA, mental health, and hygiene management before and after the COVID-19 pandemic to identify recommendations for improving adolescent health. We collected 2019-2021 data from the Korea Youth Risk Behavior Surveys from the Korea Disease Control and Prevention Agency for 166,590 middle and high school students (2019: 57,069; 2020: 54,809; 2021: 54,712). We analyzed seven variables (regular PA, high-intensity PA, and strength exercise participation, stress recognition, sleep-time satisfaction, brushing practice, and handwashing practice) to examine changes in adolescent behavior during these years. The findings indicated statistically significant differences in all seven variables over the three years (P<0.05). Specifically, regular PA was higher in 2019 and 2021 than in 2020; high-intensity PAs were highest in 2019, followed by 2021 and 2020; strength exercises were the highest in 2020, followed by 2021 and 2019; stress recognition was higher in 2019 and 2021 than in 2020; sleep-time satisfaction was the highest in 2020, followed by 2021 and 2019; brushing practice was the highest in 2019, followed by 2020 and 2021; and handwashing practice was the highest in 2020, followed by 2021 and 2019. The results recommend the need to encourage structured moderate-to-vigorous adolescent PA and systematic health education to improve Korean adolescent health behavior after COVID-19.

Optimizer's dilemma: optimization strongly influences model selection in transcriptomic prediction.

Most models can be fit to data using various optimization approaches. While model choice is frequently reported in machine-learning-based research, optimizers are not often noted. We applied two different implementations of LASSO logistic regression implemented in Python's scikit-learn package, using two different optimization approaches (coordinate descent, implemented in the liblinear library, and stochastic gradient descent, or SGD), to predict mutation status and gene essentiality from gene expression across a variety of pan-cancer driver genes. For varying levels of regularization, we compared performance and model sparsity between optimizers. After model selection and tuning, we found that liblinear and SGD tended to perform comparably. liblinear models required more extensive tuning of regularization strength, performing best for high model sparsities (more nonzero coefficients), but did not require selection of a learning rate parameter. SGD models required tuning of the learning rate to perform well, but generally performed more robustly across different model sparsities as regularization strength decreased. Given these tradeoffs, we believe that the choice of optimizers should be clearly reported as a part of the model selection and validation process, to allow readers and reviewers to better understand the context in which results have been generated. The code used to carry out the analyses in this study is available at https://github.com/greenelab/pancancer-evaluation/tree/master/01_stratified_classification. Performance/regularization strength curves for all genes in the Vogelstein et al.(2013) dataset are available at https://doi.org/10.6084/m9.figshare.22728644.

Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network.

Feature grid Scene Representation Networks (SRNs) have been applied to scientific data as compact functional surrogates for analysis and visualization. As SRNs are black-box lossy data representations, assessing the prediction quality is critical for scientific visualization applications to ensure that scientists can trust the information being visualized. Currently, existing architectures do not support inference time reconstruction quality assessment, as coordinate-level errors cannot be evaluated in the absence of ground truth data. By employing the uncertain neural network architecture in feature grid SRNs, we obtain prediction variances during inference time to facilitate confidence-aware data reconstruction. Specifically, we propose a parameter-efficient multi-decoder SRN (MDSRN) architecture consisting of a shared feature grid with multiple lightweight multi-layer perceptron decoders. MDSRN can generate a set of plausible predictions for a given input coordinate to compute the mean as the prediction of the multi-decoder ensemble and the variance as a confidence score. The coordinate-level variance can be rendered along with the data to inform the reconstruction quality, or be integrated into uncertainty-aware volume visualization algorithms. To prevent the misalignment between the quantified variance and the prediction quality, we propose a novel variance regularization loss for ensemble learning that promotes the Regularized multi-decoder SRN (RMDSRN) to obtain a more reliable variance that correlates closely to the true model error. We comprehensively evaluate the quality of variance quantification and data reconstruction of Monte Carlo Dropout (MCD), Mean Field Variational Inference (MFVI), Deep Ensemble (DE), and Predicting Variance (PV) in comparison with our proposed MDSRN and RMDSRN applied to state-of-the-art feature grid SRNs across diverse scalar field datasets. We demonstrate that RMDSRN attains the most accurate data reconstruction and competitive variance-error correlation among uncertain SRNs under the same neural network parameter budgets. Furthermore, we present an adaptation of uncertainty-aware volume rendering and shed light on the potential of incorporating uncertain predictions in improving the quality of volume rendering for uncertain SRNs. Through ablation studies on the regularization strength and decoder count, we show that MDSRN and RMDSRN are expected to perform sufficiently well with a default configuration without requiring customized hyperparameter settings for different datasets.

Optimizing lung cancer classification through hyperparameter tuning.

Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset: genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection.

Kernel-Based Independence Tests for Causal Structure Learning on Functional Data.

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert-Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert-Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data.

Full 3-D modulation transfer function estimation of tomosynthesis system using modified Richardson-Lucy deconvolution.

Tomosynthesis systems are three-dimensional (3-D) medical imaging devices that operate over limited acquisition angles using low radiation dosages. To measure the spatial resolution performance of a tomosynthesis system, the modulation transfer function (MTF) is widely used as a quantitative evaluationmetric. We previously introduced a method to estimate the full 3-D MTF of a cone-beam computed tomography system using two-dimensional (2-D) Richardson-Lucy (RL) deconvolution with Tikhonov-Miller regularization. However, this method can not be applied directly to estimate the 3-D MTF of a tomosynthesis system, since the unique artifacts (i.e., shadow artifacts, spreading tails, directional blurring, and high-level noise) of the system produce several errors that lower the estimation performance. Varying positions of the negative pixels due to shadow artifacts and spreading tails cause inconsistent deconvolution performances at each of the directional projections, and the severe noise in the reconstructed images cause noise amplification during estimation. This work proposes several modifications to the previous method to resolve the inconsistent performance and noise amplification errors to increase the full 3-D MTF estimationaccuracy. Three modifications were introduced to the 2-D RL deconvolution to prevent estimation errors and improve MTF estimation performance: non-negativity relaxation function, cost function to terminate the iterative process of RL deconvolution, and regularization strength for noise control. To validate the effectiveness of the proposed modifications, we reconstructed sphere phantoms from simulation and experimental tomosynthesis studies in the iso-center and offset-center positions as well as estimated the full 3-D MTFs using the previous and proposed methods. We compared the 3-D render images, central plane images, and center profiles of the estimated 3-D MTFs and calculated the full widths at half and tenth maximum for quantitativeevaluation. The previous method cannot estimate the full 3-D MTF of a tomosynthesis system; its inaccurate negative pixel relaxation produces circular-shaped errors, and the mean squared error based simple cost function for termination causes inconsistent estimation at each directional projection to diminish the clear edges of the low-frequency drop and missing sample regions. Noise amplification from lack of noise regularization is also seen in the previous method results. Compared to the previous method, the proposed method shows superior estimation performance at reducing errors in both the simulation and experimental studies regardless of object position. The proposed method preserves the low-frequency drop, missing sample regions from the limited acquisition angles, and missing cone region from the offset-center position; the estimated MTFs also show FWHM and FWTM values close to those of the ideal MTFs than with the previousmethod. This work presents a method to estimate the full 3-D MTF of a tomosynthesis system. The proposed modifications prevent circular-shaped errors and noise amplification due to the geometry for limited acquisition angles and high noise levels. Compared to our previous method, the proposed scheme show better performance for estimating the 3-D MTF of the tomosynthesissystem.

Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine

This study presents the development of a sentimental analysis system for high education students using Arabic text. There is a gap in the literature concerning understanding the perceptions and opinions of students in Saudi Arabia Universities regarding their education beyond COVID-19. The proposed SVM Sentimental Analysis for Arabic Students’ Course Reviews (SVM-SAA-SCR) algorithm is a general framework that involves collecting student reviews, preprocessing them, and using a machine learning model to classify them as positive, negative, or neutral. The suggested technique for preprocessing and classifying reviews includes steps such as collecting data, removing irrelevant information, tokenizing, removing stop words, stemming or lemmatization, and using pre-trained sentiment analysis models. The classifier is trained using the SVM algorithm and performance is evaluated using metrics such as accuracy, precision, and recall. Fine-tuning is done by adjusting parameters such as kernel type and regularization strength to optimize performance. A real dataset provided by the deanship of quality at Prince Sattam bin Abdulaziz University (PSAU) is used and contains students’ opinions on various aspects of their education. We also compared our algorithm with CAMeLBERT, a state-of-the-art Dialectal Arabic model. Our findings show that while the CAMeLBERT model classified 70.48% of the reviews as positive, our algorithm classified 69.62% as positive which proves the efficiency of the suggested SVM-SAA-SCR. The results of the proposed model provide valuable insights into the challenges and obstacles faced by Arab Universities post-COVID-19 and can help to improve their educational experience.

Optimizing the regularization in size-consistent second-order Brillouin-Wigner perturbation theory.

Despite its simplicity and relatively low computational cost, second-order Møller-Plesset perturbation theory (MP2) is well-known to overbind noncovalent interactions between polarizable monomers and some organometallic bonds. In such situations, the pairwise-additive correlation energy expression in MP2 is inadequate. Although energy-gap dependent amplitude regularization can substantially improve the accuracy of conventional MP2 in these regimes, the same regularization parameter worsens the accuracy for small molecule thermochemistry and density-dependent properties. Recently, we proposed a repartitioning of Brillouin-Wigner perturbation theory that is size-consistent to second order (BW-s2), and a free parameter (α) was set to recover the exact dissociation limit of H2 in a minimal basis set. Alternatively α can be viewed as a regularization parameter, where each value of α represents a valid variant of BW-s2, which we denote as BW-s2(α). In this work, we semi-empirically optimize α for noncovalent interactions, thermochemistry, alkane conformational energies, electronic response properties, and transition metal datasets, leading to improvements in accuracy relative to the abinitio parameterization of BW-s2 and MP2. We demonstrate that the optimal α parameter (α = 4) is more transferable across chemical problems than energy-gap-dependent regularization parameters. This is attributable to the fact that the BW-s2(α) regularization strength depends on all of the information encoded in the t amplitudes rather than just orbital energy differences. While the computational scaling of BW-s2(α) is iterative O(N5), this effective and transferable approach to amplitude regularization is a promising route to incorporate higher-order correlation effects at second-order cost.

Investigation of Image Enhancement Techniques for Advancing Colon Cancer Diagnosis

Colorectal cancer continues to pose a substantial worldwide health challenge, necessitating the development of advanced imaging techniques for early and accurate diagnosis. In this study, we propose a novel hybrid image enhancement approach that combines Total Variation (TV) regularization and shift-invariant filtering to improve the visibility and diagnostic quality of colon cancer images.&#x0D; The Total Variation regularization technique is employed to effectively reduce noise and enhance the edges in the input images, thereby preserving important structural details. Simultaneously, shift-invariant filtering is utilized to address spatial variations and artifacts that often arise in medical images, ensuring consistent and reliable enhancements across the entire image. Our hybrid approach synergistically integrates the strengths of both TV regularization and shift-invariant filtering, resulting in enhanced colon cancer images that offer improved contrast, reduced noise, and enhanced fine structures. This improved image quality aids medical professionals in better identifying and characterizing cancerous lesions, ultimately leading to more accurate and timely diagnoses.&#x0D; To evaluate the effectiveness of the proposed approach, we conducted extensive experimentations on a diverse dataset of colon cancer images. Quantitative and qualitative assessments demonstrate that our hybrid approach outperforms existing enhancement methods, leading to superior image quality and diagnostic accuracy. In conclusion, the hybrid image enhancement approach presented in this study offers a promising solution for enhancing colon cancer images, contributing to the early detection and effective management of this life-threatening disease. These advancements hold significant potential for improving patient outcomes and reducing the burden of colon cancer on healthcare systems worldwide.

A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams

Abstract. Ice sheet models are the main tool to generate forecasts of ice sheet mass loss, a significant contributor to sea level rise; thus, knowing the likelihood of such projections is of critical societal importance. However, to capture the complete range of possible projections of mass loss, ice sheet models need efficient methods to quantify the forecast uncertainty. Uncertainties originate from the model structure, from the climate and ocean forcing used to run the model, and from model calibration. Here we quantify the latter, applying an error propagation framework to a realistic setting in West Antarctica. As in many other ice sheet modelling studies we use a control method to calibrate grid-scale flow parameters (parameters describing the basal drag and ice stiffness) with remotely sensed observations. Yet our framework augments the control method with a Hessian-based Bayesian approach that estimates the posterior covariance of the inverted parameters. This enables us to quantify the impact of the calibration uncertainty on forecasts of sea level rise contribution or volume above flotation (VAF) due to the choice of different regularization strengths (prior strengths), sliding laws, and velocity inputs. We find that by choosing different satellite ice velocity products our model leads to different estimates of VAF after 40 years. We use this difference in model output to quantify the variance that projections of VAF are expected to have after 40 years and identify prior strengths that can reproduce that variability. We demonstrate that if we use prior strengths suggested by L-curve analysis, as is typically done in ice sheet calibration studies, our uncertainty quantification is not able to reproduce that same variability. The regularization suggested by the L curves is too strong, and thus propagating the observational error through to VAF uncertainties under this choice of prior leads to errors that are smaller than those suggested by our two-member “sample” of observed velocity fields.

The reciprocal elastic net

A new penalized likelihood method (reciprocal elastic net) is put forward for regularization and variable selection. Our proposal is based on a new class of reciprocal penalty functions, combining the strengths of the reciprocal LASSO regularization and ridge regression. We formulate the reciprocal elastic net problem as an equivalent reciprocal LASSO problem on augmented data, facilitating a direct utilization of the reciprocal LASSO algorithm to generate the entire reciprocal elastic net solution path. We further present the reciprocal adaptive elastic net, fusing the merits of ridge regression with the adaptively weighted reciprocal LASSO regularization. These methods, illustrated through simulated examples and real data analysis, demonstrate satisfactory performance in various diversified scenarios compared to published methods. Finally, we propose Bayesian methods to solve the reciprocal elastic net and reciprocal adaptive elastic net models using Gibbs samplers.

Effect of isokinetic muscle strength training on knee muscle strength, proprioception, and balance ability in athletes with anterior cruciate ligament reconstruction: a randomised control trial.

Objective: This study aimed to investigate the effects of regular isokinetic muscle strength training on knee muscle strength, proprioception, and balance ability in athletes after anterior cruciate ligament (ACL) reconstruction. Methods: Forty-one athletes who underwent ACL reconstruction were randomly divided into the experimental (n = 21) and control (n = 20) groups. The experimental group used an isokinetic muscle strength tester for 4weeks (five times/ week) of knee flexion and extension isokinetic muscle strength training. The control group used the knee joint trainer (pneumatic resistance) for the same exercise regimen as the experimental group. Results: 1) Four weeks when compared with the baseline. Experimental group: the knee flexion and extension PT (60°/s and 240°/s) increased by 31.7%, 40.3%, 23.4%, and 42.9% (p < 0.01), and the flexion muscular endurance increased by 21.4% and 19.7% (p < 0.01). The flexion and extension kinaesthesia and the 30° and 60° position sense decreased by 36.2%, 32.3%, 40.0%, and 18.9% (p < 0.05). The anterior-posterior and medial-lateral displacement and speed decreased by 30.2%, 44.2%, 38.4%, and 24.0% (p < 0.05). Control group: the knee peak torque (60°/s) increased by 18.8% (p < 0.01). The anterior-posterior and medial-lateral displacement and speed decreased by 14.9%, 40.0%, 26.8%, and 19.5% (p < 0.01). 2) After 4weeks, compared with the control group, the knee flexion and extension peak torque (60°/s), extension, peak torque (240°/s), and extension muscular endurance of the treatment group increased to varying degrees (p < 0.05). However, the kinaesthesia, 30° position sense, and anterior-posterior displacement decreased to varying degrees (p < 0.05). Conclusion: Adding regular isokinetic muscle strength training to rehabilitation training further improved the knee flexion and extensor strength and extensor endurance of athletes with ACL reconstruction, as well as enhanced the kinaesthesia and 30° position sense and the balance between the anterior and posterior directions. However, the treatment had limited effects on knee flexion kinaesthesia and muscle endurance.

Construction of machine-learning interatomic potential under heat flux regularization and its application to power spectrum analysis for silver chalcogenides

We propose a data-driven approach for constructing machine-learning interatomic potentials (MLIPs) trained under a regularization with the aim of avoiding nonphysical heat flux that arises from the many-body effect of MLIPs and is sensitive to their initial weight parameters. Specifically, we introduce a regularization term for the heat flux into the cost function of MLIPs to be minimized. Since the treatment of heat flux using MLIPs with regularization can be decomposed into elemental contributions or conducted in frequency space, this approach is expected to be useful for investigating the origin of thermal conductivity obtained from the Green-Kubo formula. However, the strength of regularization needs to be appropriately set because it may reduce not only the nonphysical part but also the intrinsic heat flux one. To this end, we investigated the conditions for constructing MLIPs that can reproduce the power spectra of heat flux associated with the empirical interatomic potential of Ag2Se, which consists of pairwise functions and do not contain a nonphysical heat flux. The power spectrum is useful because it helps to verify the effect of regularization and also contains the information on thermal conductivity and phonon. The appropriate strength could be estimated from the variation of the magnitude of regularization term as well as root mean square errors for total potential energy, atomic force, and virial stress with respect to the strengths, without reference spectrum data. As an application example, we explored the differences in power spectra between superionic and nonsuperionic conducting phases based on the heat flux regularization to MLIPs trained with the first-principles calculation data of Ag2S. Furthermore, our results demonstrate that training with the regularization improves the robustness of MLIPs as well as the reduction of the nonphysical heat flux.

Транскрипционные факторы в скелетной мышце человека, ассоциированные с однократным и регулярными силовыми упражнениями

Skeletal muscle plasticity is the ability to change morphofunctional properties in response to changes in contractile activity. Strength training increases the size of muscle fibers and maximum strength with the activation of protein synthesis. Regulation of these changes at the gene level has not been investigated properly. This study aimed to identify transcription factors associated with changes in the transcriptome of the human skeletal muscle in the context of single and regular strength exercises. We assessed changes in the transcriptomic profile of m. vastus lateralis of 10 young men (mean age 23 (20.8 - 25.9) years) before and after 12-week leg extensor muscles strength training course, as well as before, 8 and 24 hours after a single exercise. Transcriptomic profiling involved RNA sequencing, search for binding motifs and the associated transcription factors. Bioinformatic methods of statistics, FastQC, GraphPad Prizm 8, DAVID, R enabled analysis of the data acquired. The strength training course resulted in the enrichment of the functional groups of genes "secreted proteins", "extracellular matrix" and "basal membrane" (p &lt; 0.05). Transcriptomic responses and the associated transcription factors differed 8 and 24 hours after a single session as well as after regular training sessions. Transcription factors involved in adjustment to regular and one-time loads participate in myogenesis, angiogenesis, regulation of fiber phenotype, proteostasis and other processes. Thus, regulation of gene expression during adjustment to the resistance training loads is a complex process that involves many transcription factors with different functions. Investigation of the role played by these factors in the context of adjustment to exercising is a potentially rewarding task.

Targeted Metabolomics in High Performance Sports: Differences between the Resting Metabolic Profile of Endurance- and Strength-Trained Athletes in Comparison with Sedentary Subjects over the Course of a Training Year.

Little is known about the metabolic differences between endurance and strength athletes in comparison with sedentary subjects under controlled conditions and about variation of the metabolome throughout one year. We hypothesized that (1) the resting metabolic profile differs between sedentary subjects and athletes and between perennially endurance- and strength-trained athletes and (2) varies throughout one year of training. We performed quantitative, targeted metabolomics (Biocrates MxP® Quant 500, Biocrates Life Sciences AG, Innsbruck, Austria) in plasma samples at rest in three groups of male adults, 12 strength-trained (weightlifters, 20 ± 3 years), 10 endurance-trained athletes (runners, 24 ± 3 years), and 12 sedentary subjects (25 ± 4 years) at the end of three training phases (regeneration, preparation, and competition) within one training year. Performance and anthropometric data showed significant (p < 0.05) differences between the groups. Metabolomic analysis revealed different resting metabolic profiles between the groups with acetylcarnitines, di- and triacylglycerols, and glycerophospho- and sphingolipids, as well as several amino acids as the most robust metabolites. Furthermore, we observed changes in free carnitine and 3-methylhistidine in strength-trained athletes throughout the training year. Regular endurance or strength training induces changes in the concentration of several metabolites associated with adaptations of the mitochondrial energy and glycolytic metabolism with concomitant changes in amino acid metabolism and cell signaling.

Improvement of the mechanical properties of polymer-modified concrete by using PC superplasticizer

AbstractIn this article, polymer-modified concrete was studied to present the effect of using additives on its mechanical properties. This will be achieved by employing a high-performance super plasticizing admixture that significantly enhances polymer-modified concrete mechanical capabilities. The study indicates that the polymer styrene-butadiene rubber (used increases concrete mechanical properties by 10% by weight. When the dosage exceeds 10% by weight of cement, the compressive, tensile, and flexural strengths of polymer-modified concrete are diminished. With the addition of a superplasticizer with a ratio of 1.2%, regular polymer-modified concrete compressive strength has increased from 34.3 to 42.9 MPa for a 10% polymer/cement ratio. Additionally, this superplasticizer enhanced the Material's flexural and tensile strength.