On pattern recognition of brain connectivity in resting-state functional MRI

Dissertação de mestrado integrado em Biomedical Engineering (specialization on Medical Informatics)The human urge and pursuit for information have led to the development of increasingly complex technologies, and new means to study and understand the most advanced and intricate biological system: the human brain. Large-scale neuronal communication within the brain, and how it relates to human behaviour can be inferred by delving into the brain network, and searching for patterns in connectivity. Functional connectivity is a steady characteristic of the brain, and it has been proved to be very useful for examining how mental disorders affect connections within the brain. The detection of abnormal behaviour in brain networks is performed by experts, such as physicians, who limit the process with human subjectivity, and unwittingly introduce errors in the interpretation. The continuous search for alternatives to obtain faster and robuster results have put Machine Learning and Deep Learning in the leading position of computer vision, as they enable the extraction of meaningful patterns, some beyond human perception. The aim of this dissertation is to design and develop an experiment setup to analyse functional connectivity at a voxel level, in order to find functional patterns. For the purpose, a pipeline was outlined to include steps from data download to data analysis, resulting in four methods: Data Download, Data Preprocessing, Dimensionality Reduction, and Analysis. The proposed experiment setup was modeled using as materials resting state fMRI data from two sources: Life and Health Sciences Research Institute (Portugal), and Human Connectome Project (USA). To evaluate its performance, a case study was performed using the In-House data for concerning a smaller number of subjects to study. The pipeline was successful at delivering results, although limitations concerning the memory of the machine used restricted some aspects of this experiment setup’s testing. With appropriate resources, this experiment setup may support the process of analysing and extracting patterns from any resting state functional connectivity data, and aid in the detection of mental disorders.O desejo e a busca intensos do ser humano por informação levaram ao desenvolvimento de tecnologias cada vez mais complexas e novos meios para estudar e entender o sistema biológico mais avançado e intrincado: o cérebro humano. A comunicação neuronal em larga escala no cérebro, e como ela se relaciona com o comportamento humano, pode ser inferida investigando a rede neuronal cerebral e procurando por padrões de conectividade. A conectividade funcional é uma característica constante do cérebro e provou ser muito útil para examinar como os distúrbios mentais afetam as conexões cerebrais. A deteção de anormalidades em imagens de ressonância magnética é realizada por especialistas, como médicos, que limitam o processo com a subjetividade humana e, inadvertidamente, introduzem erros na interpretação. A busca contínua de alternativas para obter resultados mais rápidos e robustos colocou as técnicas de machine learning e deep learning na posição de liderança de visão computacional, pois permitem a extração de padrões significativos e alguns deles para além da percepção humana. O objetivo desta dissertação é projetar e desenvolver uma configuração experimental para analisar a conectividade funcional ao nível do voxel, a fim de encontrar padrões funcionais. Nesse sentido, foi delineado um pipeline para incluir etapas a começar no download de dados até à análise desses mesmos dados, resultando assim em quatro métodos: Download de Dados, Pré-processamento de Dados, Redução de Dimensionalidade e Análise. A configuração experimental proposta foi modelada usando dados de ressonância magnética funcional de resting-state de duas fontes: Instituto de Ciências da Vida e Saúde (Portugal) e Human Connectome Project (EUA). Para avaliar o seu desempenho, foi realizado um estudo de caso usando os dados internos por considerar um número menor de participantes a serem estudados. O pipeline foi bem-sucedido em fornecer resultados, embora limitações relacionadas com a memória da máquina usada tenham restringido alguns aspetos do teste desta configuração experimental. Com recursos apropriados, esta configuração experimental poderá servir de suporte para o processo de análise e extração de padrões de qualquer conjunto de dados de conectividade funcional em resting-state e auxiliar na deteção de transtornos mentais

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

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

Ciência, Crise e Mudança. 3.º Encontro Nacional de História das Ciências e da Tecnologia. ENHCT2012

III Encontro Nacional de História das Ciências e da Tecnologia. O Centro de Estudos de História e Filosofia da Ciência, organiza o 3.º Encontro Nacional de História da Ciência e da Técnica, sob o tema «Ciência, Crise e Mudança» que tem lugar na Universidade de Évora, nos dias 26, 27 e 28 de Setembro de 2012. O Primeiro Encontro Nacional de História da Ciência teve lugar em 21 e 22 Julho de 2009, no seguimento do programa de estímulo ao de¬senvolvimento da História da Ciência em Portugal e de valorização do património cultural e científico do País, lançado pelo Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) em 31 de Janeiro desse ano. A sua organização coube a investigadores do Instituto de História Contemporânea (IHC), da FCSH da UNL, e do Centro Científico e Cultural de Macau (CCCM), em cujas instalações se realizou. De en¬tre as conclusões do Encontro, destacou-se a de realizar periodicamen¬te novos Encontros Nacionais, a serem organizados de forma rotativa por diferentes centros e núcleos de investigadores. Na sequência deste Primeiro Encontro, o Centro Interuniversitário de História das Ciências e da Tecnologia (CIUHCT) organizou, entre 26 e 28 de Julho de 2010, o II Encontro, dedicado ao tema “Comunicação das Ciências e da Tecnologia em Portugal: Agentes, Meios e Audiências”. Cabe agora ao CEHFCi cumprir o que foi decidido no final deste Encontro. Na situação económica e política que hoje vivemos torna-se particularmente urgente aprofundar o estudo e o debate sobre a interação entre a Sociedade, a Ciência e a sua História. Coordenação Científica e Executiva do encontro estiveram a cargo de dois investigadores CEHFCi: Maria de Fátima Nunes, José Pedro Sousa Dia

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved