Muscle health is a critical component of overall health and quality of life. However, current measures of skeletal muscle health take limited account of microstructural variations within muscle, which play a crucial role in mediating muscle function. To address this, we present a physics-inspired, machine learning-based framework for the non-invasive and in vivo estimation of microstructural organization in skeletal muscle from diffusion-weighted MRI (dMRI). To reduce the computational expense associated with direct numerical simulations of dMRI physics, a polynomial meta-model is developed that accurately represents the input/output relationships of a high-fidelity numerical model. This meta-model is used to develop a Gaussian process (GP) model to provide voxel-wise estimates and confidence intervals of microstructure organization in skeletal muscle. Given noise-free data, the GP model accurately estimates microstructural parameters. In the presence of noise, the diameter, intracellular diffusion coefficient, and membrane permeability are accurately estimated with narrow confidence intervals, while volume fraction and extracellular diffusion coefficient are poorly estimated and exhibit wide confidence intervals. A reduced-acquisition GP model, consisting of one-third the diffusion-encoding measurements, is shown to predict parameters with similar accuracy to the original model. The fiber diameter and volume fraction estimated by the reduced GP model is validated via histology, with both parameters within their associated confidence intervals, demonstrating the capability of the proposed framework as a promising non-invasive tool for assessing skeletal muscle health and function.

Three independent groups of researchers have designed powerful artificial muscles that are around 100 times stronger than ours. The synthetic muscles are are designed around coiled or coiling fibres that can stretch and contract just like their natural counterparts. The muscle designs could have various applications -- from developing smart clothing that changes in response to the weather, to prosthetic limbs and robots. Three independent groups of researchers have designed powerful artificial muscles that are around 100 times stronger than ours. The same basic principle underpins the brawny robots developed by each research team -- that coiled materials can stretch just like natural muscles.

Adaptive behaviors ranging from self-assembly to self-healing showcase the ability of such systems to sense and adapt to dynamic environments based on signaling between living cells. This signaling takes on many forms--biochemical, mechanical, and electrical--and uncovering it has become as much the purview of regenerative medicine as of fundamental biology. We cannot reverse-engineer native tissues if we do not understand the fundamental design rules and principles that govern their assembly from the bottom up (1). Movement is fundamental to many living systems and driven primarily by skeletal muscle in human bodies. Disease or damage that limits the functionality of skeletal muscle severely affects human health, mobility, and quality of life.

For the past several decades, research in understanding the molecular basis of human muscle aging has progressed significantly. However, the development of accessible tissue-specific biomarkers of human muscle aging that may be measured to evaluate the effectiveness of therapeutic interventions is still a major challenge. Here we present a method for tracking age-related changes of human skeletal muscle. We analyzed publicly available gene expression profiles of young and old tissue from healthy donors. Differential gene expression and pathway analysis were performed to compare signatures of young and old muscle tissue and to preprocess the resulting data for a set of machine learning algorithms.

Genetic mutations already in the population today may make the X-men movies seem less like science fiction than you think. It turns out that there are some genetic mutations that seemingly give some people superhuman abilities. For example, some people have a very rare genetic mutation that makes muscle cells grow bigger and divide more than usual, resulting in a condition where people, and even children, can look like body builders. A gene mutation is a permanent alteration in the DNA sequence that makes up a gene, such that the sequence differs from what is found in most people. Mutations can be beneficial, harmful or neutral depending on their context and location.

Many robotic designs take nature as their muse: sticking to walls like geckos, swimming through water like tuna, sprinting across terrain like cheetahs. Such designs borrow properties from nature, using engineered materials and hardware to mimic animals' behavior. Now, scientists at MIT and the University of Pennsylvania are taking more than inspiration from nature -- they're taking ingredients. The group has genetically engineered muscle cells to flex in response to light, and is using the light-sensitive tissue to build highly articulated robots. This "bio-integrated" approach, as they call it, may one day enable robotic animals that move with the strength and flexibility of their living counterparts.

Our central goal is to quantify the long-term progression of pediatric neurological diseases, such as a typical 10-15 years progression of child dystonia. To this purpose, quantitative models are convincing only if they can provide multi-scale details ranging from neuron spikes to limb biomechanics. The models also need to be evaluated in hyper-time, i.e. significantly faster than real-time, for producing useful predictions. We designed a platform with digital VLSI hardware for multi-scale hyper-time emulations of human motor nervous systems. The platform is constructed on a scalable, distributed array of Field Programmable Gate Array (FPGA) devices. All devices operate asynchronously with 1 millisecond time granularity, and the overall system is accelerated to 365x real-time. Each physiological component is implemented using models from well documented studies and can be flexibly modified. Thus the validity of emulation can be easily advised by neurophysiologists and clinicians. For maximizing the speed of emulation, all calculations are implemented in combinational logic instead of clocked iterative circuits. This paper presents the methodology of building FPGA modules in correspondence to components of a monosynaptic spinal loop. Results of emulated activities are shown. The paper also discusses the rationale of approximating neural circuitry by organizing neurons with sparse interconnections. In conclusion, our platform allows introducing various abnormalities into the neural emulation such that the emerging motor symptoms can be analyzed. It compels us to test the origins of childhood motor disorders and predict their long-term progressions.

Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biological, etc.) have developed powerful methods for their statistical analysis and for clustering, categorising, and classifying objects. Finally, physicists have developed a theory of quantum measurement, unifying the logical, algebraic, and probabilistic aspects of queries into a single formalism. The purpose of this paper is twofold: first to show that when formulated at an abstract level, problems from IR, from statistical data analysis, and from physical measurement theories are very similar and hence can profitably be cross-fertilised, and, secondly, to propose a novel method of fuzzy hierarchical clustering, termed \textit{semantic distillation} -- strongly inspired from the theory of quantum measurement --, we developed to analyse raw data coming from various types of experiments on DNA arrays. We illustrate the method by analysing DNA arrays experiments and clustering the genes of the array according to their specificity.