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The intracerebral local field potential (LFP) is a measure of brain activity that reflects the highly dynamic flow of information across neural networks. This is a composite signal that receives contributions from multiple neural sources, yet interpreting its nature and significance may be hindered by several confounding factors and technical limitations. By and large, the main factor defining the amplitude of LFPs is the geometry of the current sources, over and above the degree of synchronization or the properties of the media. As such, similar levels of activity may result in potentials that differ in several orders of magnitude in different populations. The geometry of these sources has been experimentally inaccessible until intracerebral high density recordings enabled the co-activating sources to be revealed. Without this information, it has proven difficult to interpret a century's worth of recordings that used temporal cues alone, such as event or spike related potentials and frequency bands. Meanwhile, a collection of biophysically ill-founded concepts have been considered legitimate, which can now be corrected in the light of recent advances. The relationship of LFPs to their sources is often counterintuitive. For instance, most LFP activity is not local but remote, it may be larger further from rather than close to the source, the polarity does not define its excitatory or inhibitory nature, and the amplitude may increase when source's activity is reduced. As technological developments foster the use of LFPs, the time is now ripe to raise awareness of the need to take into account spatial aspects of these signals and of the errors derived from neglecting to do so.

Public engagement is an important role for the university academic, but is often neglected due to perceived lack of time and prioritized commitments in research and teaching. Yet, public engagement events offer an untapped opportunity for researchers to collect data from members of the general public who arrive on site at university labs. These engagement events could allow for data collection as part of didactic and demonstrative outreach events to be used in research and science. In this proof of concept study, a collaborative group of international researchers investigated the feasibility of embedding research quality assessment into events surrounding National Biomechanics Day. The Big Experiment collected data on 501 secondary school students (age range: 13 to 18 years) across 9 university sites within a 24-hour period. Data included maximal vertical jump height and self-reported physical activity levels. Vertical jump height was positively correlated to participant height, but not age or body mass. Very physically active students had significantly higher vertical jump heights than individuals who reported being somewhat or not physically active. This feasibility project demonstrates that with substantial preparation and a simple research design, focused research questions can be incorporated into educational outreach initiatives and ultimately provide a rich data source.

In their article, “Measurement of cochlear power gain in the sensitive gerbil ear,” Ren et al. (Nat Commun 2:216, 2011 ) claim to provide “the first direct experimental evidence of power amplification in the sensitive living cochlea.” While we recognize the technical challenges of the experiments and appreciate the beauty of the data, the authors’ analysis and interpretation of the measurements are invalid. We review the concept of impedance (i.e., the ratio of pressure to velocity) as it applies to cochlear mechanics and show that Ren et al. mistakenly equate the impedances near the basilar membrane and stapes with the impedance characteristic of an infinite, uniform tube of fluid. As a consequence of this error, Ren et al.’s measurements and analysis provide no evidence for power amplification in the cochlea. Compelling evidence for power amplification has, however, been previously provided by others.

To probe the mechanism by which the motor protein kinesin moves along microtubules, we have developed a highly sensitive technique for measuring the force exerted by a single motor molecule. In this technique, one end of a microtubule is attached to the tip of a flexible glass fiber of calibrated stiffness. The other end of the microtubule makes contact with a surface sparsely coated with kinesin. By imaging the tip of the glass fiber on a photodiode detector, displacement of the microtubule by kinesin through as little as 1 nm can be detected and forces as small as 1 pN resolved. Using this force-fiber apparatus we have characterized the mechanical output of this molecular motor. The speed at which a molecule of kinesin moved along the surface of a microtubule decreased linearly as the elastic force was increased. The force required to stop a single kinesin molecule was 5.4 ± 1.0 pN (mean ± SD; n = 16), independent of the stiffness of the fiber, the damping from the fluid, and whether the ATP concentration was high or low.

The International Society of Biomechanics (ISB) has charged this committee with development of a standard similar in scope to the kinematic standard proposed in Wu et al. (2002) and Wu et al. (2005). Given the variety of purposes for which intersegmental forces and moments are used in biomechanical research, it is not possible to recommend a particular set of analysis standards that will be acceptable in all applications. Instead, it is the purpose of this paper to recommend a set of reporting standards that will result in an understanding of the differences between investigations and the ability to reproduce the research. The end products of this standard are (1) a critical checklist that can be used during submission of manuscripts and abstracts to insure adequate description of methods, and (2) a web based visualization tool that can be used to alter the coordinate system, normalization technique and internal/external perspective of intersegmental forces and moments during walking and running so that the shape and magnitude of the curves can be compared to one’s own data.

Single-molecule Förster resonance energy transfer (smFRET) is increasingly being used to determine distances, structures, and dynamics of biomolecules in vitro and in vivo. However, generalized protocols and FRET standards to ensure the reproducibility and accuracy of measurements of FRET efficiencies are currently lacking. Here we report the results of a comparative blind study in which 20 labs determined the FRET efficiencies (E) of several dye-labeled DNA duplexes. Using a unified, straightforward method, we obtained FRET efficiencies with s.d. between ±0.02 and ±0.05. We suggest experimental and computational procedures for converting FRET efficiencies into accurate distances, and discuss potential uncertainties in the experiment and the modeling. Our quantitative assessment of the reproducibility of intensity-based smFRET measurements and a unified correction procedure represents an important step toward the validation of distance networks, with the ultimate aim of achieving reliable structural models of biomolecular systems by smFRET-based hybrid methods.

By reaching near-atomic resolution for a wide range of specimens, single-particle cryo-EM structure determination is transforming structural biology. However, the necessary calculations come at large computational costs, which has introduced a bottleneck that is currently limiting throughput and the development of new methods. Here, we present an implementation of the RELION image processing software that uses graphics processors (GPUs) to address the most computationally intensive steps of its cryo-EM structure determination workflow. Both image classification and high-resolution refinement have been accelerated more than an order-of-magnitude, and template-based particle selection has been accelerated well over two orders-of-magnitude on desktop hardware. Memory requirements on GPUs have been reduced to fit widely available hardware, and we show that the use of single precision arithmetic does not adversely affect results. This enables high-resolution cryo-EM structure determination in a matter of days on a single workstation.

Simulation-based medicine and the development of complex computer models of biological structures is becoming ubiquitous for advancing biomedical engineering and clinical research. Finite element analysis (FEA) has been widely used in the last few decades to understand and predict biomechanical phenomena. Modeling and simulation approaches in biomechanics are highly interdisciplinary, involving novice and skilled developers in all areas of biomedical engineering and biology. While recent advances in model development and simulation platforms offer a wide range of tools to investigators, the decision making process during modeling and simulation has become more opaque. Hence, reliability of such models used for medical decision making and for driving multiscale analysis comes into question. Establishing guidelines for model development and dissemination is a daunting task, particularly with the complex and convoluted models used in FEA. Nonetheless, if better reporting can be established, researchers will have a better understanding of a model's value and the potential for reusability through sharing will be bolstered. Thus, the goal of this document is to identify resources and considerate reporting parameters for FEA studies in biomechanics. These entail various levels of reporting parameters for model identification, model structure, simulation structure, verification, validation, and availability. While we recognize that it may not be possible to provide and detail all of the reporting considerations presented, it is possible to establish a level of confidence with selective use of these parameters. More detailed reporting, however, can establish an explicit outline of the decision-making process in simulation-based analysis for enhanced reproducibility, reusability, and sharing.

Tracking head motion in a simple, portable and accurate manner during performance of postural tasks in a virtual reality environment could have important implications for investigating normal and pathological head kinematics. We investigated concurrent validity of head tracking of two Head Mounted Displays (HMDs), Oculus Rift and HTC Vive, vs. a gold-standard motion capture system (Qualisys). Head kinematics of N = 20 healthy young adults was quantified during static and dynamic postural tasks. While wearing the Oculus Rift or HTC Vive, participants observed moving stars (static tasks) or a flying ball (dynamic task). Head kinematics were recorded simultaneously by the Rift or Vive and Qualisys camera system. We calculated head directional path, acceleration in 6 directions and volume of translation movement. Intra-Class Correlations (ICC) and 95% Limits of agreement were calculated. Most ICC values were around 0.9 with several at 0.99 indicating excellent agreement between the HMDs and Qualisys. Weaker agreement was observed for vertical displacement during a static task and moderate agreement was observed pitch and yaw displacement during a dynamic task. A negative bias of a small magnitude (indicating more movement in VR) was observed for most variables in static tasks, while a positive bias was observed for most variables in the dynamic task (indicating less movement in VR). Our results generally support the concurrent validity of Oculus Rift and HTC Vive head tracking during static and dynamic standing tasks in healthy young adults. Specific task- and direction-dependent differences should be considered when planning measurement studies using these novel tools.

Background Clinical Trials (CTs) remain the foundation of safe and effective drug development. Given the evolving data-driven and personalized medicine approach in healthcare, it is imperative for companies and regulators to utilize tailored Artificial Intelligence (AI) solutions that enable expeditious and streamlined clinical research. In this paper, we identified opportunities, challenges, and potential implications of AI in CTs. Methods Following an extensive search in relevant databases and websites, we gathered publications tackling the use of AI and Machine Learning (ML) in CTs from the past 5 years in the US and Europe, including Regulatory Authorities’ documents. Results Documented applications of AI commonly concern the oncology field and are mostly being applied in the area of recruitment. Main opportunities discussed aim to create efficiencies across CT activities, including the ability to reduce sample sizes, improve enrollment and conduct faster, more optimized adaptive CTs. While AI is an area of enthusiastic development, the identified challenges are ethical in nature and relate to data availability, standards, and most importantly, lack of regulatory guidance hindering the acceptance of AI tools in drug development. However, future implications are significant and are anticipated to improve the probability of success, reduce trial burden and overall, speed up research and regulatory approval. Conclusion The use of AI in CTs is in its relative infancy; however, it is a fast-evolving field. As regulators provide more guidance on the acceptability of AI in specific areas, we anticipate the scope of use to broaden and the volume of implementation to increase rapidly.