Microscopic swimmers, e.g., chemotactic bacteria and cells, are capable of directed motion by exerting a force on their environment. For asymmetric microswimmers, e.g., bacteria, spermatozoa and many artificial active colloidal particles, a torque is also present leading to circular motion (in two dimensions) and to helicoidal motion (in three dimensions) with a well-defined chirality. Here, we demonstrate with numerical simulations in two dimensions how the chirality of circular motion couples to chiral features present in the microswimmer environment. Levogyre and dextrogyre microswimmers as small as 50 nm can be separated and selectively trapped in chiral flowers of ellipses. Patterned microchannels can be used as funnels to rectify the microswimmer motion, as sorters to separate microswimmers based on their linear and angular velocities, and as sieves to trap microswimmers with specific parameters. We also demonstrate that these results can be extended to helicoidal motion in three dimensions.

Collective motions emerging from the interaction of autonomous mobile individuals play a key role in many phenomena, from the growth of bacterial colonies to the coordination of robotic swarms. For these collective behaviors to take hold, the individuals must be able to emit, sense, and react to signals. When dealing with simple organisms and robots, these signals are necessarily very elementary; e.g., a cell might signal its presence by releasing chemicals and a robot by shining light. An additional challenge arises because the motion of the individuals is often noisy; e.g., the orientation of cells can be altered by Brownian motion and that of robots by an uneven terrain. Therefore, the emphasis is on achieving complex and tunable behaviors from simple autonomous agents communicating with each other in robust ways. Here, we show that the delay between sensing and reacting to a signal can determine the individual and collective long-term behavior of autonomous agents whose motion is intrinsically noisy. We experimentally demonstrate that the collective behavior of a group of phototactic robots capable of emitting a radially decaying light field can be tuned from segregation to aggregation and clustering by controlling the delay with which they change their propulsion speed in response to the light intensity they measure. We track this transition to the underlying dynamics of this system, in particular, to the ratio between the robots' sensorial delay time and the characteristic time of the robots' random reorientation. Supported by numerics, we discuss how the same mechanism can be applied to control active agents, e.g., airborne drones, moving in a three-dimensional space. Given the simplicity of this mechanism, the engineering of sensorial delay provides a potentially powerful tool to engineer and dynamically tune the behavior of large ensembles of autonomous mobile agents; furthermore, this mechanism might already be at work within living organisms such as chemotactic cells.

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment.

Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.

Optical tweezers have found widespread application in many fields, from physics to biology. Here, we explain in detail how optical forces and torques can be described within the geometrical optics approximation and we show that this approximation provides reliable results in agreement with experiments for particles whose characteristic dimensions are larger than the wavelength of the trapping light. Furthermore, we provide an object-oriented software package implemented in MatLab for the calculation of optical forces and torques in the geometrical optics regime: \texttt{OTGO - Optical Tweezers in Geometrical Optics}. We provide all source codes for \texttt{OTGO} as well as the documentation and code examples -- e.g., standard optical tweezers, optical tweezers with elongated particle, windmill effect, Kramers transitions between two optical traps -- necessary to enable users to effectively employ it in their research and teaching.Comment: 9 pages, 8 figure

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH -BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.

No abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.