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Este registo pertence ao Repositório Científico do LNEC Made available in DSpace on 2008-12-19T12:31:49Z (GMT). No. of bitstreams: 2 stub.txt: 73 bytes, checksum: 3bc2cc4245f8fae5b15427ea15f98b20 (MD5) license.txt: 37 bytes, checksum: 562acc9db45b6d1b48b190c6b4653ecb (MD5) Previous issue date: 1993-01 Made available in DSpace on 2010-04-26T08:06:38Z (GMT). No. of bitstreams: 3 stub.txt.txt: 74 bytes, checksum: 2201e90db4d96cbf54168f6263e126b0 (MD5) license.txt: 37 bytes, checksum: 562acc9db45b6d1b48b190c6b4653ecb (MD5) stub.txt: 73 bytes, checksum: 3bc2cc4245f8fae5b15427ea15f98b20 (MD5) Previous issue date: 1993-01 Made available in DSpace on 2014-10-20T12:43:10Z (GMT). No. of bitstreams: 3 stub.txt: 73 bytes, checksum: 3bc2cc4245f8fae5b15427ea15f98b20 (MD5) license.txt: 37 bytes, checksum: 562acc9db45b6d1b48b190c6b4653ecb (MD5) stub.txt.txt: 74 bytes, checksum: 2201e90db4d96cbf54168f6263e126b0 (MD5) Previous issue date: 1993-01 NET

WKD3Lists2 created lists of regionally relevant commercial fish and shellfish species (and higher order taxa) for the use of Article 8 reporting by EU member states under Descriptor 3 of the Marine Strategy Framework Directive (MSFD). The regional taxa lists were based on landings data from the Fisheries Dependent Information data base (FDI) provided by EU member states and compiled by the Joint Research Centre (JRC). The taxonomy of landings data was consolidated by regional experts and the consolidated data were combined to obtain absolute and proportional landing weights and values for each (sub)re-gion, which were used to apply dual (weight and vale) selection thresholds to compile (sub)re-gional D3-taxa-lists. Regional D3-taxa-lists were produced for two MSFD regions (Baltic Sea & Black Sea) and eight MSFD subregions: The Greater North Sea, Celtic Seas, the Bay of Biscay and Iberian Coast, Mac-aronesia, Western Mediterranean, the Ionian Sea & Central Mediterranean, the Adriatic Sea and the Aegean-Levantine Sea. To exclude taxa with very low landing weights or value from the final lists, two types of thresh-olds (cumulative and minimum) with differing cut-off values were evaluated (90%, 95%, 98% and 99% for cumulative and 0.1% and 1% for minimum thresholds). Depending on the cut-off value, the number of taxa included varied substantially and in most (sub)regions the application of thresholds reduced the initial number of taxa by more than 50%. WKD3Lists2 did not recommend any threshold type or cut-off value to be applied generically in all (sub)regions, but identified trade-offs between inclusiveness and parsimony of relevant con-tent i.e. higher cut-off values will lead to longer lists including many taxa with relatively low landings weights/values. In some (sub)regions, thresholds with lower cut-off values (90% to 95%) were considered feasible by regional experts (Mediterranean subregions, Bay of Biscay and Iberian Coast, Macaronesia), whereas in other MSFD (sub)regions cut-off values in the range of 98-99% were considered as appropriate (Baltic Sea, Greater North Sea, Celtic Seas). The regional D3-taxa lists by WKD3Lists2 were created without considering the availability of data or assessments i.e. many species are included, for which no assessment information is avail-able. WKD3Lists2 decided on this approach because a representative selection of commercially targeted taxa was considered to indicate knowledge and data gaps in current data collection and assessment schemes. Regional species lists shall be used by EU member states for the national reporting of D3. Stocks and species from the regional lists shall be considered by member states, and additional stocks/species can be added where appropriate (e.g. those stocks/species of national or local of importance that do not appear on the regional lists). x WKD3Lists2 discussed and compiled recommendations on how Member States can complement the regional lists of D3-taxa. A key recommendation is to maintain taxa reported in 2018 under D3, even if they are not part of the regional D3-taxa list for 2024. Wherever possible, Member States should report on stock level. WKD3Lists2 also discussed linkages between D1 and D3-reporting of commercial taxa. info:eu-repo/semantics/publishedVersion

Alberto Leite, Narciso Oliveira, Manuela RochaInternal Medicine Department, Hospital de Braga, PortugalAbstract: McArdle disease (glycogen storage disease type V) is a pure myopathy caused by an inherited deficit of myophosphorylase. The disease exhibits clinical heterogeneity, but patients typically experience exercise intolerance, acute crises of early fatigue, and contractures, sometimes with rhabdomyolysis and myoglobinuria, triggered by static muscle contractions or dynamic exercise. We present the case of a 54-year-old man with a lifelong history of fatigability, worsening on exertion. Laboratory evaluation revealed significant elevations in levels of creatine kinase (7924 U/L), lactate dehydrogenase (624 U/L), and myoglobulin (671 ng/mL). A muscle biopsy confirmed the presence of McArdle disease. This case report illustrates how, due to embarrassment, the patient hid his symptoms for many years and was eventually extremely relieved and “liberated” once McArdle disease was diagnosed 40 years later.Keywords: McArdle disease, glycogen storage disease, myophosphorylase

Submitted by Rosa Duarte (rduarte@maieutica.ismai.pt) on 2020-07-09T16:14:08Z No. of bitstreams: 1 Relatório Ricardo Filipe Martins.pdf: 2731636 bytes, checksum: 9eb17a8cdbf37d06429dac674d5313b1 (MD5) Made available in DSpace on 2020-07-09T16:14:08Z (GMT). No. of bitstreams: 1 Relatório Ricardo Filipe Martins.pdf: 2731636 bytes, checksum: 9eb17a8cdbf37d06429dac674d5313b1 (MD5) Previous issue date: 2019-06 N/A

Submitted by Luís Quinta-Nova (lnova@esa.ipcb.pt) on 2011-09-03T20:34:17Z No. of bitstreams: 1 Relatorio_Fito_Valverde.pdf: 12897029 bytes, checksum: 64006e30d27e1de7400a89a19d2dcd45 (MD5) Approved for entry into archive by Maria Eduarda Pereira Rodrigues(erodrigues@ipcb.pt) on 2011-09-20T13:51:03Z (GMT) No. of bitstreams: 1 Relatorio_Fito_Valverde.pdf: 12897029 bytes, checksum: 64006e30d27e1de7400a89a19d2dcd45 (MD5) Made available in DSpace on 2011-09-20T14:09:54Z (GMT). No. of bitstreams: 1 Relatorio_Fito_Valverde.pdf: 12897029 bytes, checksum: 64006e30d27e1de7400a89a19d2dcd45 (MD5) Previous issue date: 1991

Submitted by Carlota Margarida Neto Moura Veiga (carlota_margarida_veiga@iscte.pt) on 2017-10-18T15:58:24Z No. of bitstreams: 2 OEm_FactSheet_05_2017_Trafico.pdf: 579190 bytes, checksum: 49f542034c246c16717b2517ba5c6f52 (MD5) OEm_FactSheet_05_2017_Trafico_QuadrosGra.xlsx: 79910 bytes, checksum: bac30f487c82348b48d3931f6d8af33a (MD5) Made available in DSpace on 2017-10-31T15:15:12Z (GMT). No. of bitstreams: 2 OEm_FactSheet_05_2017_Trafico.pdf: 579190 bytes, checksum: 49f542034c246c16717b2517ba5c6f52 (MD5) OEm_FactSheet_05_2017_Trafico_QuadrosGra.xlsx: 79910 bytes, checksum: bac30f487c82348b48d3931f6d8af33a (MD5) Previous issue date: 2017-03

As campanhas de publicidade online sobre pesquisas em motores de busca têm um grande peso nas receitas das empresas de marketing digital. Cada campanha é definida por um conjunto de palavras chave que são utilizadas pelo motor de busca para escolher a melhor publicidade candidata a apresentar juntamente com os resultados da pesquisa. Um problema com que se deparam estas campanhas é a da sugestão das melhores palavras chave para uma determinada campanha. Neste artigo é apresentado um modelo para a análise de relações entre variáveis de campanhas de publicidade, bem como um algoritmo para produzir sugestões de palavras chave. Uma vez que este algoritmo se baseia no histórico da campanha, não é aplicável a campanhas em arranque que não têm histórico. Este artigo apresenta também uma abordagem baseada no histórico de campanhas semelhantes e que permite ultrapassar esta dificuldade. Finalmente é apresentada uma caracterização estatística do histórico de campanhas AdWords reais e da relação entre estas campanhas.