Previous research on the processing of language embedded in a rich visual context has revealed the strong effect that a recently viewed action event has on language comprehension. It has been shown ...that listeners are more likely to view the target object of a recently performed event than look at the target object of a plausible future event during sentence utterance, regardless of the tense cue. In the current visual-world eye-tracking experiments, we tested the strength of the recently observed visual context with a group of English monolingual and two groups of English-French early and late bilingual speakers. By comparing these different groups, we examined whether bilingual speakers, as a consequence of greater cognitive flexibility when integrating visual context and language information, show early anticipatory eye-movements toward the target object. We further asked whether early and late bilinguals show differences in their processing. The findings of the three eye-tracking experiments revealed an overall preference for the recently seen event. However, as a result of the early provision of tense cue, this preference was quickly diminished in all three groups. Moreover, the bilingual groups showed an earlier decrease in reliance on the recently seen event compared to monolingual speakers and the early bilinguals showed anticipatory eye-movements toward the plausible future event target. Furthermore, a post-experimental memory test revealed that the bilingual groups recalled the future events marginally better than the recent events, whereas the reverse was found in the monolingual groups.
Mobile eye tracking captures egocentric vision and is well-suited for naturalistic studies. However, its data is noisy, especially when acquired outdoor with multiple participants over several ...sessions. Area of interest analysis on moving targets is difficult because A) camera and objects move nonlinearly and may disappear/reappear from the scene; and B) off-the-shelf analysis tools are limited to linearly moving objects. As a result, researchers resort to time-consuming manual annotation, which limits the use of mobile eye tracking in naturalistic studies. We introduce a method based on a fine-tuned Vision Transformer (ViT) model for classifying frames with overlaying gaze markers. After fine-tuning a model on a manually labelled training set made of 1.98% (=7845 frames) of our entire data for three epochs, our model reached 99.34% accuracy as evaluated on hold-out data. We used the method to quantify participants’ dwell time on a tablet during the outdoor user test of a mobile augmented reality application for biodiversity education. We discuss the benefits and limitations of our approach and its potential to be applied to other contexts.
Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias ...to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge of the context. However, the creation of such experiences typically requires complex programming tools that are beyond the reach of mainstream users. We introduce BiodivAR, a web cartographic tool for the authoring of location-based AR experiences. Developed using a user-centered design methodology and open-source interoperable web technologies, it is the second iteration of an effort that started in 2016. It is designed to meet needs defined through use cases co-designed with end users and enables the creation of custom geolocated points of interest. This approach enabled substantial progress over the previous iteration. Its reliance on geolocation data to anchor augmented objects relative to the user’s position poses a set of challenges: On mobile devices, GNSS accuracy typically lies between 1 m and 30 m. Due to its impact on the anchoring, this lack of accuracy can have deleterious effects on usability. We conducted a comparative user test using the application in combination with two different geolocation data types (GNSS versus RTK). While the test’s results are undergoing analysis, we hereby present a methodology for the assessment of our system’s usability based on the use of eye-tracking devices, geolocated traces and events, and usability questionnaires.
Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing ...between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied.
To assess the utility of machine learning systems for automatic discrimination of TTS and AMI.
This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021.
Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed.
Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis.
In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean SD age, 68.4 12.1 years; 103 90.4% were female) and 110 patients with AMI (mean SD age, 69.1 12.2 years; 103 90.4% were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean SD AUC, 0.72 0.02) and accuracy of 66.9 (2.8%).
In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.
In lampreys, respiration consists of a fast and a slow rhythm. This study was aimed at characterizing both anatomically and physiologically the brainstem regions involved in generating the two ...rhythms. The fast rhythm generator has been located by us and others in the rostral hindbrain, rostro-lateral to the trigeminal motor nucleus. More recently, this was challenged by researchers reporting that the fast rhythm generator was located more rostrally and dorsomedially, in a region corresponding to the mesencephalic locomotor region. These contradictory observations made us re-examine the location of the fast rhythm generator using anatomical lesions and physiological recordings. We now confirm that the fast respiratory rhythm generator is in the rostro-lateral hindbrain as originally described. The slow rhythm generator has received less attention. Previous studies suggested that it was composed of bilateral, interconnected rhythm generating regions located in the caudal hindbrain, with ascending projections to the fast rhythm generator. We used anatomical and physiological approaches to locate neurons that could be part of this slow rhythm generator. Combinations of unilateral injections of anatomical tracers, one in the fast rhythm generator area and another in the lateral tegmentum of the caudal hindbrain, were performed to label candidate neurons on the non-injected side of the lateral tegmentum. We found a population of neurons extending from the facial to the caudal vagal motor nuclei, with no clear clustering in the cell distribution. We examined the effects of stimulating different portions of the labeled population on the respiratory activity. The rostro-caudal extent of the population was arbitrarily divided in three portions that were each stimulated electrically or chemically. Stimulation of either of the three sites triggered bursts of discharge characteristic of the slow rhythm, whereas inactivating any of them stopped the slow rhythm. Substance P injected locally in the lateral tegmentum accelerated the slow respiratory rhythm in a caudal hindbrain preparation. Our results show that the fast respiratory rhythm generator consists mostly of a population of neurons rostro-lateral to the trigeminal motor nucleus, whereas the slow rhythm generator is distributed in the lateral tegmentum of the caudal hindbrain.
•Nine months after COVID-19 GD, hypogeusia is present in 30% of cases.•However, ageusia is scarce.•Patients with GD persistence have more frequently recurrences than continuous GD.•A long duration of ...GD and cacosmia could be predictive factors of GD persistence.
The potential of the volatile-producing fungus
Muscodor albus for controlling postharvest diseases of fresh fruit by biological fumigation was investigated. In vitro tests showed that
M. albus ...volatiles inhibited and killed a wide range of storage pathogens belonging to species of
Botrytis,
Colletotrichum,
Geotrichum,
Monilinia,
Penicillium and
Rhizopus. Fumigation of apples for 7 days with culture of
M. albus grown on autoclaved grain gave complete control of blue mold (
Penicillium expansum) and gray mold (
Botrytis cinerea) in wound-inoculated fruits. There was no direct contact between the fruit and the
M. albus culture. Shorter fumigation times ranging between 24 and 72
h, applied immediately or 24
h after inoculation, also controlled blue mold and gray mold. In wound-inoculated peaches, 24–72
h fumigation with
M. albus provided complete control of brown rot (
Monilinia fructicola). The volatile profile of
M. albus-colonized grain was measured by gas chromatograph connected to a flame ionization detector (GC-FID) and showed that 2-methyl-1-butanol and isobutyric acid were the major volatile compounds found in the headspace. Since
M. albus is a sterile mycelium and does not require direct contact with the crops to be treated, it could be an attractive biological fumigant for controlling postharvest diseases.
(1) Background. Post-COVID-19 syndrome is defined as the persistence of symptoms after confirmed SARS-CoV-2 infection. (2) Methods. ANOSVID is an observational retrospective study in Nord ...Franche-Comté Hospital in France that included adult COVID-19 patients confirmed by RT-PCR from 1 March 2020 to 31 May 2020. The aim was to describe patients with post-COVID-19 syndrome with persistent symptoms (PS group) and to compare them with the patients without persistent symptoms (no-PS group). (3) Results. Of the 354 COVID-19 patients, 35.9% (n = 127) reported persistence of at least one symptom after a mean of 289.1 ± 24.5 days after symptom onset. Moreover, 115 patients reported a recurrence of symptoms after recovery, and only 12 patients reported continuous symptoms. The mean age of patients was 48.6 years (19–93) ± 19.4, and 81 patients (63.8%) were female. Patients in the PS group had a longer duration of symptoms of initial acute SARS-CoV-2 infection than patients in the no-PS group (respectively, 57.1 ± 82.1 days versus 29.7 ± 42.1 days, p < 0.001). A majority of patients (n = 104, 81.9%) reported three or more symptoms. The most prevalent persistent symptoms were loss of smell (74.0%, n = 94), fatigue (53.5%, n = 68), loss of taste (31.5%, n = 40), and dyspnea (30.7%, n = 39). These were followed by pain symptoms (26.8% (n = 34), 26.0% (n = 33), 24.4% (n = 31); headache, arthralgia, and myalgia, respectively). More than half of patients reporting persistent symptoms (58%, n = 73) were healthcare workers (HCWs). Among outpatients, this population was more present in the PS group than the no-PS group ((86.6%) n = 71/82 versus (72.2%) n = 109/151, p = 0.012). Post-COVID-19 syndrome was more frequent in patients with a past history of chronic rhinosinusitis (8.7% (n = 11%) versus 1.3% (n = 3), p < 0.001). No significant difference was found regarding clinical characteristics and outcome, laboratory, imaging findings, and treatment received in the two groups. (4) Conclusions. More than a third of our COVID-19 patients presented persistent symptoms after SARS-CoV-2 infection, particularly through loss of smell, loss of taste, fatigue, and dyspnea, with a high prevalence in HCWs among COVID-19 outpatients.