Multi-line Adaptive Perimetry (MAP): A New Procedure for Quantifying Visual Field Integrity for Rapid Assessment of Macular Diseases.

PurposeIn order to monitor visual defects associated with macular degeneration (MD), we present a new psychophysical assessment called multiline adaptive perimetry (MAP) that measures visual field integrity by simultaneously estimating regions associated with perceptual distortions (metamorphopsia) and visual sensitivity loss (scotoma).MethodsWe first ran simulations of MAP with a computerized model of a human observer to determine optimal test design characteristics. In experiment 1, predictions of the model were assessed by simulating metamorphopsia with an eye-tracking device with 20 healthy vision participants. In experiment 2, eight patients (16 eyes) with macular disease completed two MAP assessments separated by about 12 weeks, while a subset (10 eyes) also completed repeated Macular Integrity Assessment (MAIA) microperimetry and Amsler grid exams.ResultsResults revealed strong repeatability of MAP and high accuracy, sensitivity, and specificity (0.89, 0.81, and 0.90, respectively) in classifying patient eyes with severe visual impairment. We also found a significant relationship in terms of the spatial patterns of performance across visual field loci derived from MAP and MAIA microperimetry. However, there was a lack of correspondence between MAP and subjective Amsler grid reports in isolating perceptually distorted regions.ConclusionsThese results highlight the validity and efficacy of MAP in producing quantitative maps of visual field disturbances, including simultaneous mapping of metamorphopsia and sensitivity impairment.Translational relevanceFuture work will be needed to assess applicability of this examination for potential early detection of MD symptoms and/or portable assessment on a home device or computer

An Approach to Assess Solder Interconnect Degradation Using Digital Signal

Department of Human and Systems EngineeringDigital signals used in electronic systems require reliable data communication. It is necessary to monitor the system health continuously to prevent system failure in advance. Solder joints in electronic assemblies are one of the major failure sites under thermal, mechanical and chemical stress conditions during their operation. Solder joint degradation usually starts from the surface where high speed signals are concentrated due to the phenomenon referred to as the skin effect. Due to the skin effect, high speed signals are sensitive when detecting the early stages of solder joint degradation. The objective of the thesis is to assess solder joint degradation in a non-destructive way based on digital signal characterization. For accelerated life testing the stress conditions were designed in order to generate gradual degradation of solder joints. The signal generated by a digital signal transceiver was travelling through the solder joints to continuously monitor the signal integrity under the stress conditions. The signal properities were obtained by eye parameters and jitter, which represented the characteristics of the digital signal in terms of noise and timing error. The eye parameters and jitter exhibited significant increase after the exposure of the solder joints to the stress conditions. The test results indicated the deterioration of the signal integrity resulted from the solder joint degradation, and proved that high speed digital signals could serve as a non-destructive tool for sensing physical degradation. Since this approach is based on the digital signals used in electronic systems, it can be implemented without requiring additional sensing devices. Furthermore, this approach can serve as a proactive prognostic tool, which provides real-time health monitoring of electronic systems and triggers early warning for impending failure.ope

Repeated stressors in adulthood increase the rate of biological ageing

Background Individuals of the same age can differ substantially in the degree to which they have accumulated tissue damage, akin to bodily wear and tear, from past experiences. This accumulated tissue damage reflects the individual’s biological age and may better predict physiological and behavioural performance than the individual‘s chronological age. However, at present it remains unclear how to reliably assess biological age in individual wild vertebrates. Methods We exposed hand-raised adult Eurasian blackbirds (Turdus merula) to a combination of repeated immune and disturbance stressors for over one year to determine the effects of chronic stress on potential biomarkers of biological ageing including telomere shortening, oxidative stress load, and glucocorticoid hormones. We also assessed general measures of individual condition including body mass and locomotor activity. Results By the end of the experiment, stress-exposed birds showed greater decreases in telomere lengths. Stress-exposed birds also maintained higher circulating levels of oxidative damage compared with control birds. Other potential biomarkers such as concentrations of antioxidants and glucocorticoid hormone traits showed greater resilience and did not differ significantly between treatment groups. Conclusions The current data demonstrate that repeated exposure to experimental stressors affects the rate of biological ageing in adult Eurasian blackbirds. Both telomeres and oxidative damage were affected by repeated stress exposure and thus can serve as blood-derived biomarkers of biological ageing.</p

Dissociation of Cerebral Blood Flow and Femoral Artery Blood Pressure Pulsatility After Cardiac Arrest and Resuscitation in a Rodent Model: Implications for Neurological Recovery.

Background Impaired neurological function affects 85% to 90% of cardiac arrest (CA) survivors. Pulsatile blood flow may play an important role in neurological recovery after CA. Cerebral blood flow (CBF) pulsatility immediately, during, and after CA and resuscitation has not been investigated. We characterized the effects of asphyxial CA on short-term (&lt;2&nbsp;hours after CA) CBF and femoral arterial blood pressure (ABP) pulsatility and studied their relationship to cerebrovascular resistance (CVR) and short-term neuroelectrical recovery. Methods and Results Male rats underwent asphyxial CA followed by cardiopulmonary resuscitation. A multimodal platform combining laser speckle imaging, ABP, and electroencephalography to monitor CBF, peripheral blood pressure, and brain electrophysiology, respectively, was used. CBF and ABP pulsatility and CVR were assessed during baseline, CA, and multiple time points after resuscitation. Neuroelectrical recovery, a surrogate for neurological outcome, was assessed using quantitative electroencephalography 90&nbsp;minutes after resuscitation. We found that CBF pulsatility differs significantly from baseline at all experimental time points with sustained deficits during the 2&nbsp;hours of postresuscitation monitoring, whereas ABP pulsatility was relatively unaffected. Alterations in CBF pulsatility were inversely correlated with changes in CVR, but ABP pulsatility had no association to CVR. Interestingly, despite small changes in ABP pulsatility, higher ABP pulsatility was associated with worse neuroelectrical recovery, whereas CBF pulsatility had no association. Conclusions Our results reveal, for the first time, that CBF pulsatility and CVR are significantly altered in the short-term postresuscitation period after CA. Nevertheless, higher ABP pulsatility appears to be inversely associated with neuroelectrical recovery, possibly caused by impaired cerebral autoregulation and/or more severe global cerebral ischemia

Development of a Step Counting Algorithm Using the Ambulatory Tibia Load Analysis System for Tibia Fracture Patients

Introduction: Ambulation can be used to monitor the healing of lower extremity fractures. However, the ambulatory behavior of tibia fracture patients remains unknown due to an inability to continuously quantify ambulation outside of the clinic. The goal of this study was to design and validate an algorithm to assess ambulation in tibia fracture patients using the ambulatory tibial load analysis system during recovery, outside of the clinic. Methods Data were collected from a cyclic tester, 14 healthy volunteers performing a 2-min walk test on the treadmill, and 10 tibia fracture patients who wore the ambulatory tibial load analysis system during recovery. Results The algorithm accurately detected 2000/2000 steps from simulated ambulatory data. (see full text for full abstract

Acute toxicity of arsenic and oxidative stress responses in the embryonic development of the common South American toad Rhinella arenarum

Arsenic (As), a natural element of ecological relevance, is found in natural water sources throughout Argentina in concentrations between 0.01mg/L and 15mg/L. The autochthonous toad Rhinella arenarum was selected to study the acute toxicity of As and the biochemical responses elicited by the exposure to As in water during its embryonic development. The median lethal concentration (LC50) value averaged 24.3mg/L As and remained constant along the embryonic development. However, As toxicity drastically decreased when embryos were exposed from heartbeat-stage on day 4 of development, suggesting the onset of detoxification mechanisms. Given the environmental concentrations of As in Argentina, there is a probability of exceeding lethal levels at 1% of sites. Arsenic at sublethal concentrations caused a significant decrease in the total antioxidant potential but generated an increase in endogenous glutathione (GSH) content and glutathione S-transferase (GST) activity. This protective response might prevent a deeper decline in the antioxidant system and further oxidative damage. Alternatively, it might be linked to As conjugation with GSH for its excretion. The authors conclude that toad embryos are more sensitive to As during early developmental stages and that relatively high concentrations of this toxic element are required to elicit mortality, but oxidative stress may be an adverse effect at sublethal concentrations.Fil: Mardirosian, Mariana Noelia. Universidad Nacional del Comahue. Facultad de Ingeniería. Departamento de Química. Laboratorio de Investigaciones Bioquímicas, Químicas y de Medio Ambiente; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lascano, Cecilia Ines. Universidad Nacional del Comahue. Facultad de Ingeniería. Departamento de Química. Laboratorio de Investigaciones Bioquímicas, Químicas y de Medio Ambiente; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ferrari, Ana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energias Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energias Alternativas; ArgentinaFil: Bongiovanni, Guillermina Azucena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energias Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energias Alternativas; ArgentinaFil: Venturino, Andres. Universidad Nacional del Comahue. Facultad de Ingeniería. Departamento de Química. Laboratorio de Investigaciones Bioquímicas, Químicas y de Medio Ambiente; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Fast human motion prediction for human-robot collaboration with wearable interfaces

In this paper, we aim at improving human motion prediction during human-robot collaboration in industrial facilities by exploiting contributions from both physical and physiological signals. Improved human-machine collaboration could prove useful in several areas, while it is crucial for interacting robots to understand human movement as soon as possible to avoid accidents and injuries. In this perspective, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The proposed interface can find many applications in the Industry 4.0 framework, where autonomous and collaborative robots will be an essential part of innovative facilities. A motion intention prediction and a motion direction prediction levels have been developed to improve detection speed and accuracy. A Gaussian Mixture Model (GMM) has been trained with IMU and EMG data following an evidence accumulation approach to predict reaching direction. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy according to the application. The output of the two predictors has been used as external inputs to a Finite State Machine (FSM) to control the behaviour of a physical robot according to user's action or inaction. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of $94.3\pm2.9\%$ after $160.0msec\pm80.0msec$ from movement onset

MirBot: A collaborative object recognition system for smartphones using convolutional neural networks

MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN). The answers provided by the system can be validated by the user so as to improve the results for future queries. All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology. This dataset grows continuously thanks to the users' feedback, and is publicly available for research. This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, convolutional neural codes and different transfer learning techniques. After comparing various models and transformation methods, the results show that the CNN features maintain the accuracy of MirBot constant over time, despite the increasing number of new classes. The app is freely available at the Apple and Google Play stores.Comment: Accepted in Neurocomputing, 201