Sistema de classificação de sinais de electroencefalograma

Mestrado em Engenharia Electrónica e TelecomunicaçõesEste trabalho apresenta um método para detectar actividade epiléptica em sinais de Electroencefalograma (EEG). O sistema é constituído por dois blocos, o primeiro trata da extracção de características do sinal EEG e o segundo procede á classificação do sinal em 2 classes. As características são medidas (como energia média, máximo do sinal, entre outras) extraídas a partir das saídas da decomposição discreta em wavelet do sinal. A classificação será tratada como um problema binário, assumindo-se que os dois tipos de classes (Classe não epiléptica e Classe epiléptica), são lin- earmente separáveis. O classificador utilizado é do tipo Support Vector Machine (SVM). O desempenho do sistema é também apresentado segundo um método ex- perimental, onde diferentes estratégias para a organização do conjunto de treino são debatidas. Para avaliar a performance deste sistema, comparativamente a outros, utilizou-se uma base de dados publicada [1]. Os resultados obtidos usando um classificador linear apresentaram-se prometedores, com uma precisão situada entre 89,74% - 99.87%.This work presents a method for detecting epileptic activity in Electroencephalogram (EEG) signals. The system is divided into two blocks, the first dealing with feature extraction from the EEG and the second with the classification problem. The features are measures (like energy, maximum and so on) taken into the outputs of the discrete wavelet decomposition of the signal. To perform the classification the support vector machine was chosen. This binary classifier was designed assuming that the two classes (epileptic and non-epileptic activity) are linearly separable. The performance of the system is also presented using an experimental study where diferent strategies to organize the training data sets are also discussed. The accuracy of the system is in range of 89,74% - 99,87% in a publicly available data set used by other works

Accounting for detection unveils the intricacy of wild boar and rabbit co-occurrence patterns in a Mediterranean landscape

This study was conducted at a research and monitoring station of the LTsER Montado platform (http://www.ltsermontado.pt/) through a research protocol established between Companhia das Lezírias S.A. (CL) and Faculdade de Ciências da Universidade de Lisboa (FCUL), under the strategic plan of the Centre for Ecology, Evolution and Environmental Changes (cE3c) and with the support of the Foundation for Science and Technology (FCT, UID/BIA/00329/2019). T.A.M. thanks partial support by CEAUL (funded by FCT through the project UID/MAT/00006/2019). G.C.S. was funded by a doctoral grant from FCT (PD/BD/114037/2015).The patterns of species co-occurrence have long served as a primary approach to explore concepts of interspecific interaction. However, the interpretation of such patterns is difficult as they can result from several complex ecological processes, in a scale-dependent manner. Here, we aim to investigate the co-occurrence pattern between European rabbit and wild boar in an estate in Central Portugal, using two-species occupancy modelling. With this framework, we tested species interaction for occupancy and detection, but also the interdependencies between both parameters. According to our results, the wild boar and European rabbit occurred independently in the study area. However, model averaging of the detection parameters revealed a potential positive effect of wild boar’s presence on rabbit’s detection probability. Upon further analysis of the parameter interdependencies, our results suggested that failing to account for a positive effect on rabbit’s detection could lead to potentially biased interpretations of the co-occurrence pattern. Our study, in spite of preliminary, highlights the need to understand these different pathways of species interaction to avoid erroneous inferences.Publisher PDFPeer reviewe

Pharmacists in dispensing drugs (PharmDisp): protocol for a clinical trial to test the effectiveness of distance education in training pharmacists for dispensing drugs

Dispensing drug is a moment in which the pharmacist is able to analyze pharmacotherapy and contribute to its rational use. However, research has shown that some pharmacists lack adequate knowledge to perform this service. This study aims to describe a research protocol for a clinical trial to test the effectiveness of a distance learning program to train pharmacists in dispensing drugs. This is a protocol for an open diagnostic, non-randomized, single group clinical trial. A 12-week duration distance learning course was structured on the Moodle platform for training community pharmacists who are registered in the Regional Board of Pharmacy and work as employees or owners in Brazilian community pharmacies. The course curricula involves concepts and practice of dispensing drugs applied to the treatment of hypertension, diabetes mellitus, dyslipidemia and asthma. Pharmacists are divided randomly into groups, to which previously selected tutors give directions to the discussion and clarify questions. A validated questionnaire is being used before and after the course to measure participants’ knowledge. Participant satisfaction with the course is also being measured. Pharmacists who work in the study headquarters municipality receive two visits from a mystery shopper, before and after the course, to evaluate their performance in dispensing drugs. The virtual platform and the content of the course material were evaluated by judges. The study has been approved by the Research Ethics Committee of the School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo. The sample size was estimated to provide desired power for testing the significance of the difference between baseline-to-endpoint change scores. Information about the course is being released through channels such as social networks. The results will be submitted for publication in scientific journals, but information enabling the identification of the study subjects will be kept confidential. The trial has been registered in The Brazilian Clinical Trials Registry with number RBR7mbrp3 on January 15th, 2015

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

Country-level gender inequality is associated with structural differences in the brains of women and men

男女間の不平等と脳の性差 --男女間の不平等は脳構造の性差と関連する--. 京都大学プレスリリース. 2023-05-10.Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women’s worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7, 876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women’s brains and provide initial evidence for neuroscience-informed policies for gender equality

Country-level gender inequality is associated with structural differences in the brains of women and men

Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women's worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7,876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women's brains and provide initial evidence for neuroscience-informed policies for gender equality

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