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

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

Distinct Effects of Acute Versus Chronic Corticosterone Exposure on Zebra Finch Responses to West Nile Virus

Wild animals are exposed to both short- (acute) and long-term (chronic) stressors. The glucocorticoid hormones, such as corticosterone (CORT), facilitate coping with such stressors, but these hormones can have quite distinct effects contingent on the duration of their elevation. Previously, we found that experimental elevation of CORT for 2 days (via implantation) affected zebra finch (Taeniopygia guttata) responses to West Nile virus (WNV). CORT-elevated birds had higher viremia for at least 2 days longer than controls, and West Nile virus (WNV)-associated mortality occurred only in CORT-elevated birds. Here, we queried how acute elevations of CORT, via injection an hour prior to WNV exposure, would affect host responses, as short-term CORT elevations can be protective in other species. Although CORT injections and implantations elevated circulating CORT to a similar degree, the type of CORT exposure had quite distinct effects on WNV responses. CORT-implanted individuals reached higher viremia and suffered more mortality to WNV than control and CORT-injected individuals. However, CORT-implanted birds maintained body mass better during infection than the other two groups. Our results further support the possibility that chronic physiological stress affects aspects of host competence and potentially community-level WNV disease dynamics

Distinct Effects of Acute Versus Chronic Corticosterone Exposure on Zebra Finch Responses to West Nile Virus

Wild animals are exposed to both short- (acute) and long-term (chronic) stressors. The glucocorticoid hormones, such as corticosterone (CORT), facilitate coping with such stressors, but these hormones can have quite distinct effects contingent on the duration of their elevation. Previously, we found that experimental elevation of CORT for 2 days (via implantation) affected zebra finch (Taeniopygia guttata) responses to West Nile virus (WNV). CORT-elevated birds had higher viremia for at least 2 days longer than controls, and West Nile virus (WNV)-associated mortality occurred only in CORT-elevated birds. Here, we queried how acute elevations of CORT, via injection an hour prior to WNV exposure, would affect host responses, as short-term CORT elevations can be protective in other species. Although CORT injections and implantations elevated circulating CORT to a similar degree, the type of CORT exposure had quite distinct effects on WNV responses. CORT-implanted individuals reached higher viremia and suffered more mortality to WNV than control and CORT-injected individuals. However, CORT-implanted birds maintained body mass better during infection than the other two groups. Our results further support the possibility that chronic physiological stress affects aspects of host competence and potentially community-level WNV disease dynamics

Komplikationen beim kathetergestützten Aortenklappenersatz nach transfemoralem Zugang

Hintergund Seit mehr als 10 Jahren wird der kathetergestützte Aortenklappenersatz (Transkatheter-Aortenklappenimplantation, „transcatheter aortic valve implantation“, TAVI) durchgeführt. Bereits in der Anfangsphase haben sich eingriffstypische Komplikationen nach transfemoralem Zugang herauskristallisiert. Ziel der Arbeit Beispielhaft wird anhand von 4 Sektionsfällen beschrieben, wie die Indikationsstellung zur TAVI und die Vermeidbarkeit der Komplikation zu prüfen ist. Material und Methoden Bei einer 86-jährigen Frau war es im Rahmen eines Repositionsversuchs des Implantats zu einem Abriss der rechten Beckengefäße gekommen. Bei einer 82-jährigen Frau war es während der Intervention zu einem Einriss des Aortenklappenrings mit Perikardtamponade gekommen. Eine 89-jährige Frau erlitt während der Intervention eine gedeckte Aortenverletzung und war während der anschließenden operativen Versorgung des Defekts verstorben. Im vierten Fall war bei einer 83 Jahre alt gewordenen Patientin im Rahmen des transfemoralen Klappenersatzes die Positionierung der Klappe misslungen, und ventrikelwärts entwickelte sich eine Embolisation der entfalteten Klappe. Es wurde eine zweite gleichartige Klappe positioniert, die in der Aorta hielt. Ergebnisse Die Indikationsstellung zur TAVI war in den 4 Fällen der multimorbiden Patientinnen gerechtfertigt. Die Komplikationen waren sehr unterschiedlich und die Gefäßverletzungen in 2 Fällen aufgrund der begonnenen Operationen nicht mehr zu prüfen. Schlussfolgerungen Die Versorgung einer Komplikation ist beim indikationsgerechten Patientenkollektiv aufgrund der Multimorbidität extrem schwierig und mit zahlreichen weiteren Komplikationen behaftet. Schlüsselwörter Herzklappenerkrankungen – Herzklappenprothese – Minimalinvasive Verfahren – Behandlungsfehler – InoperabilitätBackground Catheter-assisted aortic valve replacement or transcatheter aortic valve implantation (TAVI) has been carried out for over 10 years. Even in the initial phases typical complications after a transfemoral approach became apparent. Aim This article describes how the indications for TAVI and the avoidance of complications must be checked as exemplified by four autopsy cases. Material and methods In the first case the iliac vessels in an 86-year-old female patient ruptured during an attempt to reposition the implanted valve. In the second case a laceration of the aorta occurred close to the original aortic valve and the 82-year-old female patient died due to pericardial tamponade. In the third case an 89-year-old woman suffered a covered laceration of the aorta and the patient died during an attempt to replace the vessel. In a further case of an 83-year-old woman during transfemoral valve replacement the positioning of the valve was unsuccessful and a second valve had to be implanted due to embolization of the unfolded valve. Results In all four cases the indications for TAVI in the multimorbid patients were justified. The complications were very different and in two cases assessment of the original vascular lacerations could no longer be carried out due the fact that surgery had already begun. Conclusion Most patients undergoing TAVI are multimorbid hence the treatment of complications becomes extremely difficult and bears a great risk of causing further complications

Foundations of Feature Selection in Clinical Prediction Modeling

Selecting a set of features to include in a clinical prediction model is not always a simple task. The goals of creating parsimonious models with low complexity while, at the same time, upholding predictive performance by explaining a large proportion of the variance within the dependent variable must be balanced. With this aim, one must consider the clinical setting and what data are readily available to clinicians at specific timepoints, as well as more obvious aspects such as the availability of computational power and size of the training dataset. This chapter elucidates the importance and pitfalls in feature selection, focusing on applications in clinical prediction modeling. We demonstrate simple methods such as correlation-, significance-, and variable importance-based filtering, as well as intrinsic feature selection methods such as Lasso and tree- or rule-based methods. Finally, we focus on two algorithmic wrapper methods for feature selection that are commonly used in machine learning: Recursive Feature Elimination (RFE), which can be applied regardless of data and model type, as well as Purposeful Variable Selection as described by Hosmer and Lemeshow, specifically for generalized linear models

Machine Learning Algorithms in Neuroimaging: An Overview

Machine learning (ML) and artificial intelligence (AI) applications in the field of neuroimaging have been on the rise in recent years, and their clinical adoption is increasing worldwide. Deep learning (DL) is a field of ML that can be defined as a set of algorithms enabling a computer to be fed with raw data and progressively discover-through multiple layers of representation-more complex and abstract patterns in large data sets. The combination of ML and radiomics, namely the extraction of features from medical images, has proven valuable, too: Radiomic information can be used for enhanced image characterization and prognosis or outcome prediction. This chapter summarizes the basic concepts underlying ML application for neuroimaging and discusses technical aspects of the most promising algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), in order to provide the readership with the fundamental theoretical tools to better understand ML in neuroimaging. Applications are highlighted from a practical standpoint in the last section of the chapter, including: image reconstruction and restoration, image synthesis and super-resolution, registration, segmentation, classification, and outcome prediction