Kongsberg Vision Meeting Abstracts 2021

Kongsberg Vision Meeting was held at the University of South-Eastern Norway in Kongsberg, for the 13th time, on November 16–18, 2021. The meeting was organised as a three-day meeting with a clinical day, a research day and a lighting design day. Rigmor C. Baraas, Eilin Lundanes, Ann Elisabeth Ystenæs, Ellen Svarverud, Klaus Sjøhaug and Are Røysamb organised the three-day meeting. The theme this year was Speciality Contact Lenses and Architectural Lighting Design. Keynote speakers for the clinical optometry day and the research day were Eef van Der Worp, Nicola Logan, Fabrizio Zeri and Daddi Fadel. The keynote speakers for the lighting day were Sylvia Pont and Manuel Spitschan. The abstracts from invited and contributed talks on the different days are presented in the order they were given.

SOPTI Meeting Abstracts 2021

After more than a year of blockade due to the Covid-19 pandemic, it was finally possible to return to the events in the presence. The 26th National Conference of the Italian Optometric Association (SOPTI) was held in Bologna on October 10–11, 2021. The theme of the conference was “Good practice in Optometry and Contact Lenses”, with the accent on two topics: the optometric management of the patient in old age and the progression of myopia. Four keynote speakers were invited during the conference: Prof. Rigmor C. Baraas from the University of South-Eastern Norway in Kongsberg, Prof. David B. Elliot from the University of Bradford, Dr. Fabrizio Zeri from the University of Milano Bicocca and the IACLE President, Prof. Phil Morgan, from the University of Manchester. The abstracts from accepted posters and free papers are presented here.

UAV-Based Bridge Inspection via Transfer Learning

As bridge inspection becomes more advanced and more ubiquitous, artificial intelligence (AI) techniques, such as machine and deep learning, could offer suitable solutions to the nation’s problems of overdue bridge inspections. AI coupling with various data that can be captured by unmanned aerial vehicles (UAVs) enables fully automated bridge inspections. The key to the success of automated bridge inspection is a model capable of detecting failures from UAV data like images and films. In this context, this paper investigates the performances of state-of-the-art convolutional neural networks (CNNs) through transfer learning for crack detection in UAV-based bridge inspection. The performance of different CNN models is evaluated via UAV-based inspection of Skodsberg Bridge, located in eastern Norway. The low-level features are extracted in the last layers of the CNN models and these layers are trained using 19,023 crack and non-crack images. There is always a trade-off between the number of trainable parameters that CNN models need to learn for each specific task and the number of non-trainable parameters that come from transfer learning. Therefore, selecting the optimized amount of transfer learning is a challenging task and, as there is not enough research in this area, it will be studied in this paper. Moreover, UAV-based bridge inception images require specific attention to establish a suitable dataset as the input of CNN models that are trained on homogenous images. However, in the real implementation of CNN models in UAV-based bridge inspection images, there are always heterogeneities and noises, such as natural and artificial effects like different luminosities, spatial positions, and colors of the elements in an image. In this study, the effects of such heterogeneities on the performance of CNN models via transfer learning are examined. The results demonstrate that with a simplified image cropping technique and with minimum effort to preprocess images, CNN models can identify crack elements from non-crack elements with 81% accuracy. Moreover, the results show that heterogeneities inherent in UAV-based bridge inspection data significantly affect the performance of CNN models with an average 32.6% decrease of accuracy of the CNN models. It is also found that deeper CNN models do not provide higher accuracy compared to the shallower CNN models when the number of images for adoption to a specific task, in this case crack detection, is not large enough; in this study, 19,023 images and shallower models outperform the deeper models.

Mast Cells Are Identified in the Lung Parenchyma of Wild Mice, Which Can Be Recapitulated in Naturalized Laboratory Mice

BackgroundIt is well documented that laboratory mice bred and maintained in ultra-hygienic specific pathogen-free (SPF) barriers display reduced richness and complexity of microbiota compared with wild mice. The laboratory mice profoundly lack lung parenchymal mast cells. Hence, we aimed to investigate the lung distribution of mast cells in free-living wild mice.MethodsWild house mice were trapped in South-Eastern Norway and Hemtabad, West Bengal, India. C57BL/6 laboratory mice were bred in a purposefully built, closed environment with bedding material obtained from the natural environment in order to normalize the gut microbiota of these laboratory mice to that of the wild mice, and the offspring were collected for study at eight weeks of age.ResultsMast cells were easily identified at a substantial density in the lung parenchymal tissues of wild mice from both Norway and India, which stands in clear contrast to the rare distribution of lung parenchymal mast cells in the conventional laboratory SPF mice. Consistently, wild mice also expressed higher pulmonary levels of stem cell factor, a critical growth factor for mast cell survival. Higher levels of histamine were recorded in the lung tissues of the wild mice. Interestingly, “naturalized” C57BL/6 laboratory mice which spent their entire life in a semi-natural environment developed lung parenchymal mast cells at an appreciable density.ConclusionOur observations support that environmental factors, possibly through modulation of microbiota, may impact the tissue distribution of mast cells in mouse lung parenchyma.

Prehospital assessment and management of postpartum haemorrhage- healthcare personnel’s experiences and perspectives

Background Postpartum hemorrhage (PPH) is a serious obstetric emergency, and one of the top five causes of maternal mortality globally. The most common causes of PPH include uterine atony, placental disorders, birth trauma and coagulation defects. Timely diagnosis and early management are critical to reduce morbidity, the need for blood transfusion or even mortality. External, manual aortic compression (AC) has been suggested as an intervention that reduce PPH and extend time for control of bleeding or resuscitation. This procedure is not commonly utilized by healthcare personnel. The incidence of home-births is increasing, and competence in PPH assessment and management is essential in prehospital personnel. The objective was to explore prehospital personnel’s competence in PPH and AC, utilizing different tools. Methods The study was conducted in a county in South-eastern Norway, including five ambulance stations. All prehospital personnel (n = 250) were invited to participate in a questionnaire study. The questionnaire included the PPH self-efficacy (PPHSE) and PPH collective efficacy (PPHCE) tools, as well as tool developed utilizing the Delphi technique. Descriptive statistics were used to analyze the quantitative data, while quantitative content analysis was used to analyse free-text responses. Results A total of 87 prehospital personnel responded to the questionnaire, 57.5% male, mean age 37.9 years. In total, 80.4% were ambulance workers and/or paramedics, and 96.6 and 97.7% respectively reported to need more education or training in PPH. Moreover, 82.8% reported having managed patient(s) with PPH, but only 2.9% had performed AC. Prehospital personnels’ responses varied extensively regarding knowledge about what PPH is, how to estimate and handle PPH, and how to perform AC. Mean self-efficacy varied from 3.3 to 5.6, while collective efficacy varied from 1.9 to 3.8. Conclusions This study indicates that prehospital personnel lack knowledge about PPH and AC, due to various responses to the developed questionnaire. Even though AC is an acknowledged intervention in PPH, few participants reported that this was utilized. Our findings emphasize the need for education and training in PPH and PPH handling generally, and in AC specifically.

High Diversity of Mites (Acari: Oribatida, Mesostigmata) Supports the High Conservation Value of a Broadleaf Forest in Eastern Norway

Broadleaf forests are critical habitats for biodiversity and this biodiversity is in turn essential for their proper functioning. Mites (Acari) are a numerous and functionally essential component of these forests. We report the diversity of two important groups, Oribatida and Mesostigmata, in a broadleaf forest in Eastern Norway which is considered to be a biodiversity hotspot. Eighteen samples, each 500 cm3, were collected from diverse microhabitats (moss on ground, lichens on tree twigs lying on ground, moss on tree trunks at ground level, moss on tree trunks 1.5 m above ground, moss on decaying stump, moss on decaying log, and decaying wood from trees) from which 10,843 specimens and 95 species from 32 families of Oribatida, and 655 specimens of 34 species from 14 families of Mesostigmata were found. Only 30% of the species were previously recorded in broadleaf forests in Western Norway. Oribatid communities on decaying stump and in lichens were distinct from the other communities, while mesostigmatid communities on tree trunks (both at ground level and 1.5 m above ground) and in lichens differed most from other communities. Over 30% of the species were found in only a single microhabitat. Twenty-three species and the genus Zerconopsis are reported from Norway for the first time. Six records are also new to Fennoscandia, including (Oribatida) Coronoquadroppia monstruosa, Eueremaeus valkanovi, Ramusella furcata, and (Mesostigmata) Dendrolaelaps rectus, D. multidentatus, and D. tenuipilus. In addition, several rare species were detected, e.g., Achipteria magna, Oribotritia berlesei, and Subiasella quadrimaculata, and two were found in their northernmost locality (O. berlesei, E. valkanovi). These results confirm the unique character and high conservation value of the studied forest in Norway, Fennoscandia and at a European scale.

Exploring the Composition of Lithic Assemblages in Mesolithic South-Eastern Norway

This paper leverages multivariate statistics to explore the composition of 54 Mesolithic assemblages located in south-eastern Norway. To provide analytical control pertaining to factors such as variable excavation practices, systems for artefact categorisation and raw-material availability, the sites chosen for analysis have all been excavated relatively recently and have a constrained geographical distribution. The assemblages were explored following two strains of analysis. The first of these entailed the use of artefact categories that are established within Norwegian Mesolithic archaeology, while the other involved drawing on measures that have been linked directly to land-use and mobility patterns associated with lithic assemblages more widely. The findings pertaining to the established artefact categories largely reflect the temporal development previously reported in Norwegian Mesolithic research, which has been based on more subjectively driven methods. Furthermore, the chronological trends associated with variables taken from the so-called Whole Assemblage Behavioural Indicators (e.g. Clark and Barton 2017), originally devised for characterising Palaeolithic assemblages in terms of associated mobility patterns, also align with the development previously proposed in the literature. This provides an initial indication that these measures are applicable in a Norwegian Mesolithic setting as well, setting the stage for a more targeted and rigorous model evaluation outside this exploratory setting. Furthermore, this finding supports the notion that these measures can offer a powerful comparative tool in the analysis of lithic assemblages more generally.

Type of broadleaf forest matters most for ptyctimous mite communities (Acari, Oribatida) in Norway

We studied ptyctimous moss mites, which are characteristic of forest habitats, in Norwegian broadleaf forests considered as biodiversity hotspot areas in Fennoscandia. The study aimed to evaluate the effect of different factors (regional locality, annual precipitation, mean annual temperature, forest type, forest wetness and microhabitat) on the ptyctimous mites and on discovering their richness in broadleaf forests. Samples were collected from nine broadleaf forests in Western, Southern and Eastern Norway, in different climatic conditions, six forest types, three forest wetness states and eight microhabitats. Overall, 3341 ptyctimous mites were collected and their abundance differed significantly among the regions, forest types and microhabitats. Forest type turned out to be the most important factor, responsible for 24.5% of the total variation in the abundance of the ptyctimous mites. Other important factors were forest wetness and microhabitat. In total, 27 species, i.e., 87% of all ptyctimous mites known from before in Norway were found and the species richness was highest in the east and lowest in the west of the country. Atropacarus (Atropacarus) striculus was most common and most abundant; it made nearly 30% of all ptyctimous mites collected. On the other hand, a quarter of the species were represented by less than 10 specimens; most of these were new records for Norway. Among ten species discovered as new to Norway, four were also new to Fennoscandia. These findings confirm the unique character and high biological diversity of Norwegian broadleaf forests.