Study of the light’s dazzling effect on the EEG signal of subjects performing tasks that require concentration

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2019The objective of this work is to study the effect of luminous glare on the electroencephalographic (EEG) signals of subjects that perform concentration-based tasks. The increasing access to high-power and directional light sources (such as laser pointers, but also some flashlights) has led to a growing concern with the potential effects of its use. More than the direct damaging of the retina, the focus has been directed at the effects related to the change in states of concentration on individuals performing tasks whose concentration is critical (such as helicopter pilots or heavy vehicles drivers). This effect is known as ”dazzling” and is typically a temporary deleterious effect on the ability to see or concentrate. However, while damage to the retina can be quantified, glare effects, being indirect (based on the effect on the execution of a given task), are typically qualitative (or at least of more subjective quantification). In this context, the use of brain-computer interfaces capable of analyzing the brain response to external stimuli, opens a door towards the creation of a new tool to evaluate the effects of dazzle. Its potential was evaluated by defining a set of strategies involving the illumination process, EEG signal recording and analysis. A continuous performance task commonly used as an assessment in cognitive neuroscience (N-back) was used to test the attention under the effect of dazzling, in parallel with EEG signals acquisition. Statistical data analysis was performed with the R programming language. ANOVA statistical significant results (p<0.001) for answer scores and latency were obtained for differences between the levels of difficulty, both with or without dazzling. Tukey’s test further revealed that these statistical differences were on the 0-back/2-back and 1-back/2-back pairs (p<0.005). The differences in the pair 0-back/1-back were not significant. Peak band frequency statistical tests were not significant with or without dazzling. Statistical differences were found between dazzling conditions for the frequency band power. For the 0-back and 1-back levels, with the AF7-Fp1 electrode pair, T-student tests resulted in an alpha band frequency power increase (p<0.003, in both cases). The electrode pair AF8-Fp2 resulted in an alfa and beta frequency band increase for the 1-back level (p<0.014 and p<0.029, respectively). These results suggest that concentration is affected by dazzling and can be quantified by means of measuring the change in alpha and beta frequency band power. This technique holds potential and, if further researched and developed, may constitute an effective way of measuring the degree of loss of concentration under the effect of dazzling

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost