Collaborative Computation in Self-Organizing Particle Systems

Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. They can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to image processing, with implementations of image color transformation and edge detection algorithms

Quantum information processes in protein microtubules of brain neurons

We study biologically ‘orchestrated’ coherent quantum processes in collections of protein microtubules of brain neurons, which correlate with, and regulate, neuronal synaptic and membrane activity. In this situation the continuous Schrodinger evolution of each such process terminates in accordance with the specific Diosi-Penrose (DP) scheme of ‘objective reduction’ (‘OR’) of the quantum state. This orchestrated OR activity (‘Orch OR’) is taken to result in moments of conscious awareness and/or choice. We analyze Orch OR in light of advances and developments in quantum physics, computational neuroscience and quantum biology. Much attention is also devoted to the ‘beat frequencies’ of faster microtubule vibrations as a possible source of the observed electroencephalographic (‘EEG’) correlates of consciousness

Meet the researchers: an alternative method of engaging patients with research in mesothelioma.

There are new ways to engage people with science and research but many patient support groups and charitable organisations still hold traditional meetings to provide updates on their activities and to report new developments in their field of interest. These meetings often feature presentations given by medical doctors or, in the case of research-focussed organisations, by research scientists.Receiving feedback from people who are confused and sometimes upset by some types of information, and the way it is presented at meetings, made us think about better ways for researchers to discuss their ideas for new research, or share the findings from completed projects, with patients and members of the public.This article describes a method of public engagement called "Meet the Researchers" that enables people to hear about current trends in research face to face with the researchers planning or conducting it. "Meet the Researchers" is designed to promote discussion and allow questions to be asked in a relaxed and informal way, in small groups, which is less daunting than asking questions in front of a conference audience. The aim is to break down the barriers between researchers and patients, and enable conversations that will lead to meaningful engagement and a better understanding of research. Additionally we aim to improve understanding of how results are passed on to doctors and nurses and translated into improvements in patient care.The method was tested with patients and was rated very highly by them in the feedback they gave. Abstract:Background Innovative approaches to engaging people with science exist but are often framed around interactive events or social media technologies. Notwithstanding the availability of novel approaches, many patient support groups and charitable organisations continue to hold traditional meetings and seminars to provide information and updates on their activities, and report on developments in their field of interest. In the case of research-focussed organisations, these meetings often take the form of presentations delivered by clinical experts or research scientists.Observation of mesothelioma patients, their relatives, friends and carers attending scientific or clinical-themed meetings has shown that they can be confused, and sometimes distressed, by presentations. This can be due to didactic presentations that are not properly targeted to this audience and a lack of a general overview or summary at the end of meetings that would provide some simple take home messages. This experience motivated the development of a less formal method of sharing complex information and ideas in a simplified manner. "Meet the Researchers" aims to make researchers accessible to patients in order to raise awareness and understanding of research and to explain how research translates into, and informs practice. This approach encourages the use of plain English, removes the tendency to rely on PowerPoint slides to convey the message and moreover, provides an opportunity for researchers to hear patients' views. Methods Small groups of participants met face to face with the researchers planning or conducting research into their condition, and discussed the topics in a relaxed and informal way. The researchers spent a minimum of 20-min with each group before moving on to the next. Info-graphics on a portable device or printed hand-outs in plain English were allowed but no formal presentations were made. Results Our method has been evaluated using feedback data from three annual events held from 2016 to 2018: 100% of participants indicated that they liked the format "very much"(76.0%) or "quite a lot"(24.0%); 80.4% found the topics "very interesting" and 75.9% found it "very easy" to ask questions. Free text comments revealed themes of 'hope' and 'altruism'. Researchers also reported benefits from participation such as learning about patient' priorities and networking. Conclusion "Meet the Researchers" provides a unique opportunity for mesothelioma researchers and patients, relatives and carers to interact on a more equal footing. It stimulates discussion, promotes understanding and provides a more informal setting for non-professional participants to ask questions. It is a format that could easily be adapted for use in other conditions

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Many applications, related to autonomous mobile robots, require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue and handle cooperatively some distributed targets. This problem is formulated by first defining an optimization model and then considering two sub-problems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit a certain number of robots to deal with the found target together. For this latter purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization, and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete the mission. Simulation results indicate that the firefly-based strategy usually provides superior performance and can reduce the wastage of energy, especially in complex scenarios

A Hybrid Visual-Model Based Robot Control Strategy for Micro Ground Robots

This paper proposed a hybrid vision-based robot control strategy for micro ground robots by mediating two vision models from mixed categories: a bio-inspired collision avoidance model and a segmentation based target following model. The implemented model coordination strategy is described as a probabilistic model using finite state machine (FSM) that allows the robot to switch behaviours adapting to the acquired visual information. Experiments demonstrated the stability and convergence of the embedded hybrid system by real robots, including the studying of collective behaviour by a swarm of such robots with environment mediation. This research enables micro robots to run visual models with more complexity. Moreover, it showed the possibility to realize aggregation behaviour on micro robots by utilizing vision as the only sensing modality from non-omnidirectional cameras

Subspecialization within default mode nodes characterized in 10,000 UK Biobank participants

The human default mode network (DMN) is implicated in several unique mental capacities. In this study, we tested whether brain-wide interregional communication in the DMN can be derived from population variability in intrinsic activity fluctuations, gray-matter morphology, and fiber tract anatomy. In a sample of 10,000 UK Biobank participants, pattern-learning algorithms revealed functional coupling states in the DMN that are linked to connectivity profiles between other macroscopical brain networks. In addition, DMN gray matter volume was covaried with white matter microstructure of the fornix. Collectively, functional and structural patterns unmasked a possible division of labor within major DMN nodes: Subregions most critical for cortical network interplay were adjacent to subregions most predictive of fornix fibers from the hippocampus that processes memories and places

Development and external validation of a clinical prediction model for functional impairment after intracranial tumor surgery

OBJECTIVE Decision-making for intracranial tumor surgery requires balancing the oncological benefit against the risk for resection-related impairment. Risk estimates are commonly based on subjective experience and generalized num-bers from the literature, but even experienced surgeons overestimate functional outcome after surgery. Today, there is no reliable and objective way to preoperatively predict an individual patient's risk of experiencing any functional impair-ment. METHODS The authors developed a prediction model for functional impairment at 3 to 6 months after microsurgical resection, defined as a decrease in Karnofsky Performance Status of >= 10 points. Two prospective registries in Swit- zerland and Italy were used for development. External validation was performed in 7 cohorts from Sweden, Norway, Germany, Austria, and the Netherlands. Age, sex, prior surgery, tumor histology and maximum diameter, expected major brain vessel or cranial nerve manipulation, resection in eloquent areas and the posterior fossa, and surgical approach were recorded. Discrimination and calibration metrics were evaluated. RESULTS In the development (2437 patients, 48.2% male; mean age +/- SD: 55 +/- 15 years) and external validation (2427 patients, 42.4% male; mean age +/- SD: 58 +/- 13 years) cohorts, functional impairment rates were 21.5% and 28.5%, respectively. In the development cohort, area under the curve (AUC) values of 0.72 (95% CI 0.69-0.74) were observed. In the pooled external validation cohort, the AUC was 0.72 (95% CI 0.69-0.74), confirming generalizability. Calibration plots indicated fair calibration in both cohorts. The tool has been incorporated into a web-based application available at https://neurosurgery.shinyapps.io/impairment/. CONCLUSIONS Functional impairment after intracranial tumor surgery remains extraordinarily difficult to predict, al- though machine learning can help quantify risk. This externally validated prediction tool can serve as the basis for case by-case discussions and risk-to-benefit estimation of surgical treatment in the individual patient.Scientific Assessment and Innovation in Neurosurgical Treatment Strategie