Wavefront curvature sensing for the human eye

In this paper we present a curvature wavefront sensor for the eye. The layout proposed is novel, whilst the algorithm used has been adapted from previously published work [Roddier, F.; Roddier, C. Appl. Opt. 1991, 30, 1325–1327]. The design of the set-up incorporates two field lenses that, together with a beam separator, define the distance Δz between the two sampled planes. We present a feasibility study to use this particular combination of optical configuration and retrieval algorithm in the eye. We present calibration curves and results from three real eyes

There You Are! Automated Detection of Indris’ Songs on Features Extracted from Passive Acoustic Recordings

From MDPI via Jisc Publications RouterHistory: received 2022-12-07, rev-recd 2022-12-21, accepted 2022-12-28, collection 2023-01, epub 2023-01-09Peer reviewed: TrueAcknowledgements: Acknowledgments: We are grateful to the local field guides and the assistants that helped during the data collection. We also wish to thank the GERP (Groupe d’Étude et de Recherche sur les Primates de Madagascar) for their unfailing support during the research activities and to the Parco Natura Viva for the financial and technical assistance. Data collection was carried out under research permits No. 118/19/MEDD/SG/DGEF/DSAP/DGRNE, 284/19/MEDD/SG/DGEF/DSAP/DGRNE, and 338/19/MEDD/G/DGEF/DSAP/DGRNE issued by the Ministère de l’Environnement et du Développement Durable (MEDD). Data collection in 2021 did not require a permit because it was only performed by Malagasy citizens.Article version: VoRPublication status: PublishedFunder: University of TorinoFunder: Parco Natura Viva—Garda Zoological ParksFunder: UIZA—the Italian Association of Zoos and AquariaSimple Summary: Identifying vocalisations of given species from passive acoustic recordings is a common step in bioacoustics. While manual labelling and identification are widespread, this approach is time-consuming, prone to errors, and unsustainable in the long term, given the vast amount of data collected through passive monitoring. We developed an automated classifier based on a convolutional neural network (CNN) for passive acoustic data collected via an in situ monitoring protocol. In particular, we aimed to detect the vocalisations of the only singing lemur, Indri indri. Our network achieved a very high performance (accuracy >90% and recall >80%) in song detection. Our study contributes significantly to the automated wildlife detection research field because it represents a first attempt to combine a CNN and acoustic features based on a third-octave band system for song detection. Moreover, the automated detection provided insights that will improve field data collection and fine-tune conservation practices. Abstract: The growing concern for the ongoing biodiversity loss drives researchers towards practical and large-scale automated systems to monitor wild animal populations. Primates, with most species threatened by extinction, face substantial risks. We focused on the vocal activity of the indri (Indri indri) recorded in Maromizaha Forest (Madagascar) from 2019 to 2021 via passive acoustics, a method increasingly used for monitoring activities in different environments. We first used indris’ songs, loud distinctive vocal sequences, to detect the species’ presence. We processed the raw data (66,443 10-min recordings) and extracted acoustic features based on the third-octave band system. We then analysed the features extracted from three datasets, divided according to sampling year, site, and recorder type, with a convolutional neural network that was able to generalise to recording sites and previously unsampled periods via data augmentation and transfer learning. For the three datasets, our network detected the song presence with high accuracy (>90%) and recall (>80%) values. Once provided the model with the time and day of recording, the high-performance values ensured that the classification process could accurately depict both daily and annual habits of indris‘ singing pattern, critical information to optimise field data collection. Overall, using this easy-to-implement species-specific detection workflow as a preprocessing method allows researchers to reduce the time dedicated to manual classification

Curvature-based wavefront sensors for the human eye

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A NEW DAONELLA FROM THE LADINIAN PLATFORM OF THE ESINO LIMESTONE (SOUTHERN ALPS, ITALY)

The bivalve Daonella Mojsisovics, 1874 is very common in the Middle Triassic pelagic facies, whereas the record of this genus from shallow water limestones is rare. In the present paper a new species of Daonella, named D. pseudograbensis, is described from the Esino Limestone, a Ladinian (Middle Triassic) carbonate platform in the central Southern Alps. The species is described from Brembana Valley, where the Esino Limestone is rather rich in bioclastic lenses yielding faunas with bivalves, cephalopods, gastropods, brachiopods, corals and calcareous algae. Daonella pseudograbensis n. sp. is based on very well preserved specimens, which are often articulated and closed, all coming from the same locality. The new species shows a narrow range of intraspecific and ontogenetic morphologic variations. It is easy distinguishable from the other species of the genus for the outline and ornamentation; it therefore differs from D. grabensis Kittl, 1912, the most similar species, for the longer anterior dorsal margin.Pd

There You Are! Automated Detection of Indris' Songs on Features Extracted from Passive Acoustic Recordings.

From Europe PMC via Jisc Publications RouterHistory: ppub 2023-01-01, epub 2023-01-09Publication status: PublishedThe growing concern for the ongoing biodiversity loss drives researchers towards practical and large-scale automated systems to monitor wild animal populations. Primates, with most species threatened by extinction, face substantial risks. We focused on the vocal activity of the indri (Indri indri) recorded in Maromizaha Forest (Madagascar) from 2019 to 2021 via passive acoustics, a method increasingly used for monitoring activities in different environments. We first used indris' songs, loud distinctive vocal sequences, to detect the species' presence. We processed the raw data (66,443 10-min recordings) and extracted acoustic features based on the third-octave band system. We then analysed the features extracted from three datasets, divided according to sampling year, site, and recorder type, with a convolutional neural network that was able to generalise to recording sites and previously unsampled periods via data augmentation and transfer learning. For the three datasets, our network detected the song presence with high accuracy (>90%) and recall (>80%) values. Once provided the model with the time and day of recording, the high-performance values ensured that the classification process could accurately depict both daily and annual habits of indris' singing pattern, critical information to optimise field data collection. Overall, using this easy-to-implement species-specific detection workflow as a preprocessing method allows researchers to reduce the time dedicated to manual classification

There You Are! Automated Detection of Indris’ Songs on Features Extracted from Passive Acoustic Recordings

The growing concern for the ongoing biodiversity loss drives researchers towards practical and large-scale automated systems to monitor wild animal populations. Primates, with most species threatened by extinction, face substantial risks. We focused on the vocal activity of the indri (Indri indri) recorded in Maromizaha Forest (Madagascar) from 2019 to 2021 via passive acoustics, a method increasingly used for monitoring activities in different environments. We first used indris’ songs, loud distinctive vocal sequences, to detect the species’ presence. We processed the raw data (66,443 10-min recordings) and extracted acoustic features based on the third-octave band system. We then analysed the features extracted from three datasets, divided according to sampling year, site, and recorder type, with a convolutional neural network that was able to generalise to recording sites and previously unsampled periods via data augmentation and transfer learning. For the three datasets, our network detected the song presence with high accuracy (>90%) and recall (>80%) values. Once provided the model with the time and day of recording, the high-performance values ensured that the classification process could accurately depict both daily and annual habits of indris‘ singing pattern, critical information to optimise field data collection. Overall, using this easy-to-implement species-specific detection workflow as a preprocessing method allows researchers to reduce the time dedicated to manual classification

There You Are! Automated Detection of Indris’ Songs on Features Extracted from Passive Acoustic Recordings

The growing concern for the ongoing biodiversity loss drives researchers towards practical and large-scale automated systems to monitor wild animal populations. Primates, with most species threatened by extinction, face substantial risks. We focused on the vocal activity of the indri (Indri indri) recorded in Maromizaha Forest (Madagascar) from 2019 to 2021 via passive acoustics, a method increasingly used for monitoring activities in different environments. We first used indris’ songs, loud distinctive vocal sequences, to detect the species’ presence. We processed the raw data (66,443 10-min recordings) and extracted acoustic features based on the third-octave band system. We then analysed the features extracted from three datasets, divided according to sampling year, site, and recorder type, with a convolutional neural network that was able to generalise to recording sites and previously unsampled periods via data augmentation and transfer learning. For the three datasets, our network detected the song presence with high accuracy (>90%) and recall (>80%) values. Once provided the model with the time and day of recording, the high-performance values ensured that the classification process could accurately depict both daily and annual habits of indris‘ singing pattern, critical information to optimise field data collection. Overall, using this easy-to-implement species-specific detection workflow as a preprocessing method allows researchers to reduce the time dedicated to manual classification

Automatic detection of indris songs using convolutional neural networks

From Crossref proceedings articles via Jisc Publications RouterHistory: epub 2022-01-17, issued 2022-01-17Publication status: Publishe

Impact on mental health, disease management, and socioeconomic modifications in hematological patients during the COVID-19 pandemic in Italy

Background: Hematological patients are a highly vulnerable population with an increased risk of developing severe COVID-19 symptoms due to their immunocompromised status. COVID-19 has proven to cause serious mental health issues, such as stress, anxiety, and depression in the general population. However, data on the psycho-social impact of COVID-19 on hematological patients are lacking. Objectives: This study aims to examine the psychological well-being of hematological patients in Italy during the initial period of the COVID-19 pandemic. Furthermore, it seeks to explore the association between modifications in the management of hematological diseases and employment status of these patients during the COVID-19 pandemic and the resulting mental health outcomes. Design and Methods: A survey using the DASS-21 questionnaire was administered to 1105 hematological patients. Data analysis was conducted using the R software, and logistic regression analysis was performed to predict the association between hematological patient/general population and employment status with DASS scores. Results: The hematological patient population reported significantly higher levels of depression (OR 0.947, 95% CI 0.966–0.982, p  < 0.001), anxiety (OR 0.948, 95% CI 0.939–0.958, p  < 0.001), and stress (OR 0.984, 95% CI 0.977–0.992, p  < 0.001) compared with the general population. A significant relationship has been found in stress between employed and unemployed patients (OR 1.015, 95% CI 1.000–1.030, p  = 0.044), as well as in the control group (OR 1.024, 95% CI 1.010–1.039, p  = 0.001). In addition, employment status is significantly related to depression, anxiety, and stress in both the hematological patient group and the general population. Conclusion: During the initial phase of the COVID-19 pandemic, hematological patients had elevated levels of depression, anxiety, and stress compared with the general population. The delay in their treatment and employment status played a role in their mental health outcomes. These findings emphasize the importance of further research to gain deeper insight into the long-term psychological effects and explore effective strategies for managing mental health in similar crises