Fragala, MS, Cadore, EL, Dorgo, S, Izquierdo, M, Kraemer, WJ, Peterson, MD, and Ryan, ED. Resistance training for older adults: position statement from the national strength and conditioning association. J Strength Cond Res XX(X): 000-000, 2019-Aging, even in the absence of chronic disease, is associated with a variety of biological changes that can contribute to decreases in skeletal muscle mass, strength, and function. Such losses decrease physiologic resilience and increase vulnerability to catastrophic events. As such, strategies for both prevention and treatment are necessary for the health and well-being of older adults. The purpose of this Position Statement is to provide an overview of the current and relevant literature and provide evidence-based recommendations for resistance training for older adults. As presented in this Position Statement, current research has demonstrated that countering muscle disuse through resistance training is a powerful intervention to combat the loss of muscle strength and muscle mass, physiological vulnerability, and their debilitating consequences on physical functioning, mobility, independence, chronic disease management, psychological well-being, quality of life, and healthy life expectancy. This Position Statement provides evidence to support recommendations for successful resistance training in older adults related to 4 parts: (a) program design variables, (b) physiological adaptations, (c) functional benefits, and (d) considerations for frailty, sarcopenia, and other chronic conditions. The goal of this Position Statement is to a) help foster a more unified and holistic approach to resistance training for older adults, b) promote the health and functional benefits of resistance training for older adults, and c) prevent or minimize fears and other barriers to implementation of resistance training programs for older adults.
Objectives This study aimed to explore the effects of an isocaloric Mediterranean diet (MD) intervention on metabolic health, gut microbiome and systemic metabolome in subjects with lifestyle risk factors for metabolic disease. Design Eighty-two healthy overweight and obese subjects with a habitually low intake of fruit and vegetables and a sedentary lifestyle participated in a parallel 8-week randomised controlled trial. Forty-three participants consumed an MD tailored to their habitual energy intakes (MedD), and 39 maintained their regular diets (ConD). Dietary adherence, metabolic parameters, gut microbiome and systemic metabolome were monitored over the study period. Results Increased MD adherence in the MedD group successfully reprogrammed subjects’ intake of fibre and animal proteins. Compliance was confirmed by lowered levels of carnitine in plasma and urine. Significant reductions in plasma cholesterol (primary outcome) and faecal bile acids occurred in the MedD compared with the ConD group. Shotgun metagenomics showed gut microbiome changes that reflected individual MD adherence and increase in gene richness in participants who reduced systemic inflammation over the intervention. The MD intervention led to increased levels of the fibre-degrading Faecalibacterium prausnitzii and of genes for microbial carbohydrate degradation linked to butyrate metabolism. The dietary changes in the MedD group led to increased urinary urolithins, faecal bile acid degradation and insulin sensitivity that co-varied with specific microbial taxa. Conclusion Switching subjects to an MD while maintaining their energy intake reduced their blood cholesterol and caused multiple changes in their microbiome and metabolome that are relevant in future strategies for the improvement of metabolic health.
Supplemental Digital Content is Available in the Text. Abstract Nuzzo, JL. Narrative review of sex differences in muscle strength, endurance, activation, size, fiber type, and strength training participation rates, preferences, motivations, injuries, and neuromuscular adaptations. J Strength Cond Res 37(2): 494–536, 2023—Biological sex and its relation with exercise participation and sports performance continue to be discussed. Here, the purpose was to inform such discussions by summarizing the literature on sex differences in numerous strength training–related variables and outcomes—muscle strength and endurance, muscle mass and size, muscle fiber type, muscle twitch forces, and voluntary activation; strength training participation rates, motivations, preferences, and practices; and injuries and changes in muscle size and strength with strength training. Male subjects become notably stronger than female subjects around age 15 years. In adults, sex differences in strength are more pronounced in upper-body than lower-body muscles and in concentric than eccentric contractions. Greater male than female strength is not because of higher voluntary activation but to greater muscle mass and type II fiber areas. Men participate in strength training more frequently than women. Men are motivated more by challenge, competition, social recognition, and a desire to increase muscle size and strength. Men also have greater preference for competitive, high-intensity, and upper-body exercise. Women are motivated more by improved attractiveness, muscle “toning,” and body mass management. Women have greater preference for supervised and lower-body exercise. Intrasexual competition, mate selection, and the drive for muscularity are likely fundamental causes of exercise behaviors in men and women. Men and women increase muscle size and strength after weeks of strength training, but women experience greater relative strength improvements depending on age and muscle group. Men exhibit higher strength training injury rates. No sex difference exists in strength loss and muscle soreness after muscle-damaging exercise.
Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of viable structures. Coupling such capabilities with physically significant data to construct trained models for materials discovery is crucial to moving this emerging field forward. Here, we present a universal GM for crystal structure prediction (CSP) via a conditional crystal diffusion variational autoencoder (Cond-CDVAE) approach, which is tailored to allow user-defined material and physical parameters such as composition and pressure. This model is trained on an expansive dataset containing over 670,000 local minimum structures, including a rich spectrum of high-pressure structures, along with ambient-pressure structures in Materials Project database. We demonstrate that the Cond-CDVAE model can generate physically plausible structures with high fidelity under diverse pressure conditions without necessitating local optimization, accurately predicting 59.3% of the 3547 unseen ambient-pressure experimental structures within 800 structure samplings, with the accuracy rate climbing to 83.2% for structures comprising fewer than 20 atoms per unit cell. These results meet or exceed those achieved via conventional CSP methods based on global optimization. The present findings showcase substantial potential of GMs in the realm of CSP.
ABSTRACT The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is still a major public health concern worldwide. Currently, SARS-CoV-2 variants have been widely used to develop the updated vaccine. However, whether these mutated residues still have good immunogenicity remains elusive. In particular, we know little about what kind of antibodies can be induced by the infection or vaccination of SARS-CoV-2 variants and their biological characteristics. Here, we identified an R452-dependent monoclonal neutralizing antibody, ConD-852, from a primarily Delta variant-infected individual, indicating that the mutated R452 residue has good immunogenicity. We determined the high-resolution cryo-electron microscopy (cryo-EM) structure of ConD-852 complexed with the Delta receptor-binding domain (RBD), revealing how it binds to the R452-related epitopes and their detailed interactions. Interestingly, ConD-852 could only bind to the amino acid residue “R” at the 452 position on RBD, displaying a strict restriction to recognize SARS-CoV-2. Overall, our findings regarding ConD-852 confirmed the good immunogenicity of SARS-CoV-2 variants carrying the L452R mutation and enriched our knowledge of the binding model involving the neutralizing antibody and the mutated virus. IMPORTANCE Although SARS-CoV-2 variants have been widely used to update the COVID-19 vaccine candidate, whether these mutations still have good immunogenicity is unknown. This study demonstrates that the mutated R452 residue can induce potent neutralizing antibodies and reports a high-resolution cryo-EM structure of an R452-dependent monoclonal antibody binding to the epitopes around the R452 residue on SARS-CoV-2 RBD. Although SARS-CoV-2 variants have been widely used to update the COVID-19 vaccine candidate, whether these mutations still have good immunogenicity is unknown. This study demonstrates that the mutated R452 residue can induce potent neutralizing antibodies and reports a high-resolution cryo-EM structure of an R452-dependent monoclonal antibody binding to the epitopes around the R452 residue on SARS-CoV-2 RBD.
Generative models have achieved remarkable success in image, video, and text domains. Inspired by this, researchers have explored utilizing generative models to generate neural network parameters. However, these efforts have been limited by the parameter size and the practicality of generating high-performance parameters. In this paper, we propose COND P-DIFF, a novel approach that demonstrates the feasibility of controllable high-performance parameter generation, particularly for LoRA (Low-Rank Adaptation) weights, during the fine-tuning process. Specifically, we employ an autoencoder to extract efficient latent representations for parameters. We then train a conditional latent diffusion model to synthesize high-performing model parameters from random noise based on specific task conditions. Experimental results in both computer vision and natural language processing domains consistently demonstrate that COND P-DIFF can generate high-performance parameters conditioned on the given task. Moreover, we observe that the parameter distribution generated by COND P-DIFF exhibits differences compared to the distribution obtained through normal optimization methods, indicating a certain level of generalization capability. Our work paves the way for further exploration of condition-driven parameter generation, offering a promising direction for task-specific adaptation of neural networks.
Cool astrophysical objects, such as (exo)planets, brown dwarfs, or asymptotic giant branch stars, can be strongly affected by condensation. Condensation does not only directly affect the chemical composition of the gas phase by removing elements but the condensed material also influences other chemical and physical processes in these objects. This includes, for example, the formation of clouds in planetary atmospheres and brown dwarfs or the dust-driven winds of evolved stars. In this study we introduce FastChem Cond, a new version of the FastChem equilibrium chemistry code that adds a treatment of equilibrium condensation. Determining the equilibrium composition under the impact of condensation is complicated by the fact that the number of condensates that can exist in equilibrium with the gas phase is limited by a phase rule. However, this phase rule does not directly provide information on which condensates are stable. As a major advantage of FastChem Cond is able to automatically select the set stable condensates satisfying the phase rule. Besides the normal equilibrium condensation, FastChem Cond can also be used with the rainout approximation that is commonly employed in atmospheres of brown dwarfs or (exo)planets. FastChem Cond is available as open-source code, released under the GPLv3 licence. In addition to the C++ code, FastChem Cond also offers a Python interface. Together with the code update we also add about 290 liquid and solid condensate species to FastChem.
We present a self contained derivation of the Friedel oscillations in a degenerate ideal electron plasma using a not commonly known theorem on the asymptotic behavior of the Fourier transform of a generalized function presenting some singularities.
In this work, the dynamics of a self-propelled stochastic particle under the influence of an axisymmetric light field was experimentally studied. The particle under consideration has the main characteristic of carrying a light sensor in an eccentric location. For the chosen experimental conditions, the emerging trajectories were orbital, and, more interestingly, they presented two preferential radial distances. A mathematical model incorporating the key experimental components was introduced. By means of numerical simulations and theoretical analysis, it was found that, in addition to the orbiting behavior, the sensor location could produce trapped or diffusive behaviors. Furthermore, the study revealed that stochastic perturbation and the eccentric location of the sensor are responsible for inducing bistability in the orbital trajectories, in agreement with the experimental observations.
Liquid helium under negative pressure represents a unique possibility for studying the macroscopic quantum nucleation phenomena in condensed media. We analyze the quantum cavitation rate of single electron bubbles at low temperatures down to absolute zero. The energy dissipation and sound emission processes result in the different temperature behavior of quantum cavitation rate in normal fluid $^3$He and superfluid $^4$He below the thermal-quantum crossover temperature. The position of rapid nucleation line in the temperature-pressure phase diagram is discussed as well.
Recently, one analog of the Anderson's Theorem for the $s$-wave superconductor has attracted much interest in the context of the $p$-wave polar pairing state of superfluid $^3$He in a model aerogel in the limit of strong uniaxial anisotropy. We discuss to what extent the theorem is satisfied in the polar phase in real aerogels by examining the normal to polar transition temperature $T_c$ and the low temperature behavior of the superfluid energy gap under an anisotropy of a moderate strength and comparing the obtained results with experimental data. The situation in which the Anderson's theorem clearly breaks down is also discussed.
M. Mesgarnezhad, R. G. Cooper, A. W. Baggaley
et al.
We numerically study the evolution of a small turbulent region of quantised vorticity in superfluid helium, a regime which can be realised in the laboratory. We show that the turbulence achieves a fluctuating steady-state in terms of dynamics (energy), geometry (length, writhing) and topology (linking). After defining the knot spectrum, we show that, at any instant, the turbulence consists of many unknots and few large loops of great geometrical and topological complexity.