Mission specification in underwater robotics

This paper describes the utilization of software design patterns and plan-based mission specification in the definition of AUVs missions. Within this approach, a mission is described in terms of a set of task-oriented plans in order to simplify mission definition and favor reutilization of some aspects of a mission. Each plan organizes how and when basic tasks like measurement sampling, navigation or communication are to be carried out. The usage of design patterns for AUVs has been considered in order to ease system architecture design.This work has been partially supported by the following research projects: Project PI2007/039 funded by the Autonomous Government of Canary Islands (Gobierno de Canarias — Consejería de Educación, Cultura y Deportes, Spain) with FEDER funding; and Project TIN2008-06068 funded by the Ministerio de Ciencia e Investigación, Gobierno de España

Foredune responses to the impact of aggregate extraction in an arid aeolian sedimentary system

Coastal dunes have long suffered the effects of human interventions that have altered the landscape and operation of these ecosystems. Aggregate extractions have been shown to modify the biogeomorphological processes in aeolian sedimentary systems. The impacts associated to aggregate extraction include the reduction of available sediment and changes to the topography and vegetation patterns, thereby altering the sedimentary dynamics and limiting the recovery capacity of the dunefield. The aim of this article is to analyse the environmental effects produced by historical aggregate extraction in the foredune area of an arid aeolian sedimentary system (El Medano, Tenerife, Spain) through a study of the airflow dynamics and spatial distribution of vegetation, sediment and topographic changes. The methodology was designed with two temporal scales: (i) a long-term approach which compares historical sources and current ones; (ii) a short-term approach through experimental data collection to characterize the present functioning. For the latter, a field study was carried out in June 2021, collecting wind speed and direction data at a height of 0.50 m, sediment data (sand sheet thickness, grain size and sorting), and vegetation data (cover and species richness) at 40 sample points. The main results show that when the anthropic stress ceased the foredune did not follow a natural environmental pattern, and that the way it functions at the present time is determined by the changes induced by the aggregate extraction. Changes include alterations to the topography, the creation of a lagoon, and the generation of an aeolian deflation area and flow acceleration zones with the associated sand transport. This research contributes to an understanding of the environmental consequences of aggregate extractions on the foredunes of arid aeolian sedimentary systems and can enable the relevant authorities to make better-informed decisions that help the management of these ecosystems

Addressing the disparities in dementia risk, early detection and care in Latino populations: Highlights from the Second Latinos and Alzheimer's Symposium

The Alzheimer's Association hosted the second Latinos & Alzheimer's Symposium in May 2021. Due to the COVID-19 pandemic, the meeting was held online over 2 days, with virtual presentations, discussions, mentoring sessions, and posters. The Latino population in the United States is projected to have the steepest increase in Alzheimer's disease (AD) in the next 40 years, compared to other ethnic groups. Latinos have increased risk for AD and other dementias, limited access to quality care, and are severely underrepresented in AD and dementia research and clinical trials. The symposium highlighted developments in AD research with Latino populations, including advances in AD biomarkers, and novel cognitive assessments for Spanish-speaking populations, as well as the need to effectively recruit and retain Latinos in clinical research, and how best to deliver health-care services and to aid caregivers of Latinos living with AD

SARS-CoV-2 viral load in nasopharyngeal swabs is not an independent predictor of unfavorable outcome

The aim was to assess the ability of nasopharyngeal SARS-CoV-2 viral load at first patient’s hospital evaluation to predict unfavorable outcomes. We conducted a prospective cohort study including 321 adult patients with confirmed COVID-19 through RT-PCR in nasopharyngeal swabs. Quantitative Synthetic SARS-CoV-2 RNA cycle threshold values were used to calculate the viral load in log10 copies/mL. Disease severity at the end of follow up was categorized into mild, moderate, and severe. Primary endpoint was a composite of intensive care unit (ICU) admission and/or death (n = 85, 26.4%). Univariable and multivariable logistic regression analyses were performed. Nasopharyngeal SARS-CoV-2 viral load over the second quartile (≥ 7.35 log10 copies/mL, p = 0.003) and second tertile (≥ 8.27 log10 copies/mL, p = 0.01) were associated to unfavorable outcome in the unadjusted logistic regression analysis. However, in the final multivariable analysis, viral load was not independently associated with an unfavorable outcome. Five predictors were independently associated with increased odds of ICU admission and/or death: age ≥ 70 years, SpO2, neutrophils > 7.5 × 103/µL, lactate dehydrogenase ≥ 300 U/L, and C-reactive protein ≥ 100 mg/L. In summary, nasopharyngeal SARS-CoV-2 viral load on admission is generally high in patients with COVID-19, regardless of illness severity, but it cannot be used as an independent predictor of unfavorable clinical outcome

Dendritic cell deficiencies persist seven months after SARS-CoV-2 infection

Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2 infection induces an exacerbated inflammation driven by innate immunity components. Dendritic cells (DCs) play a key role in the defense against viral infections, for instance plasmacytoid DCs (pDCs), have the capacity to produce vast amounts of interferon-alpha (IFN-α). In COVID-19 there is a deficit in DC numbers and IFN-α production, which has been associated with disease severity. In this work, we described that in addition to the DC deficiency, several DC activation and homing markers were altered in acute COVID-19 patients, which were associated with multiple inflammatory markers. Remarkably, previously hospitalized and nonhospitalized patients remained with decreased numbers of CD1c+ myeloid DCs and pDCs seven months after SARS-CoV-2 infection. Moreover, the expression of DC markers such as CD86 and CD4 were only restored in previously nonhospitalized patients, while no restoration of integrin β7 and indoleamine 2,3-dyoxigenase (IDO) levels were observed. These findings contribute to a better understanding of the immunological sequelae of COVID-19

Programming with Components in Robotics ∗ AntonioC.Domínguez-Brito,

This paper describes a component-oriented programming framework for robotics, CoolBOT, which is actually under development at the University of Las Palmas de Gran Canaria (ULPGC). The framework has been designed to assist robotic system developers in building more structured and reusable systems. Components are the basic building blocks used in this framework, modeled as Port Automata, PA [7], that interact through their ports and that can be composed to build up new components from existing ones. Components, whether atomic or compound, are internally modeled as Discrete Event Systems and controlled using the same state control graph. CoolBOT hides any aspects related to communications and provides standard mechanisms for different modes of data exchange between components, exception handling and support for distributed computing environments.

Application of visual classification algorithms for identification of underwater audio signals

An audio processing and classification pipeline is presented in this work. The main focus is on the classification of sounds in a marine acoustic environment, however, the presented approach can be applied to other audio data. Audio samples from heterogeneous sources automatically spliced, normalized and transformed into spectrogram based visual representation are tagged on the pipeline input. The said representation is then used to train a convolutional neural network that can identify the presented categories in future recordings

Efficient plane detection in multilevel surface maps

An automatic system aimed at producing a compact tridimensional description of indoor environments using a mobile 3D laser scanner is described in this paper. The resulting description is made up of a Multi-Level Map (ML map) and a series of planar patches extracted from the map. We propose a novel plane detection algorithm, based on the efficient RANSAC algorithm, that operates directly over the data structures of an ML map and does not need to rely on the low level laser data cloud. The mobile 3D scanner is built from a Hokuyo laser range sensor attached to a 2DOF pan-tilt, which is installed on top of a 3DX Pioneer mobile robot. The 3D spatial information acquired by the laser sensor from different poses is used to build a large single map of the environment using the SLAM 6D library. Experimental results demonstrate that the system described is capable of efficiently building compact and accurate 3D representations of complex large indoor environments at multiple semantic levels.This work has been partially supported by the Research Project TIN2008-06068 funded by the Ministerio de Ciencia e Innovación, Gobierno de España, Spain

Obstacle avoidance in underwater glider path planning

Underwater gliders have revealed as a valuable scientific platform, with a growing number of successful environmental sampling applications. They are specially suited for long range missions due to their unmatched autonomy level, although their low surge speed make them strongly affected by ocean currents. Path planning constitute a real concern for this type of vehicle, as it may reduce the time taken to reach a given waypoint or save power. In such a dynamic environment it is not easy to find an optimal solution or any such requires large computational resources. In this paper, we present a path planning scheme with low computational cost for this kind of underwater vehicle that allows static or dynamic obstacle avoidance, frequently demanded in coastal environments, with land areas, strong currents, shipping routes, etc. The method combines an initialization phase, inspired by a variant of the A* search process and ND algorithm, with an optimization process that embraces the physical vehicle motion pattern. Consequently, our method simulates a glider affected by the ocean currents, while it looks for the path that optimized a given objective. The method is easy to configure and adapt to various optimization problems, including missions in different operational scenarios. This planner shows promising results in realistic simulations, including ocean currents that vary considerably in time, and provides a superior performance over other approaches that are compared in this paper