SLAM Project - Long Term Ecological Study of the Impacts of Climate Change in the Natural Forest of Azores: III - Testing the impact of edge effects in a native forest of Terceira Island

BACKGROUND: The data we present are part of the long-term project “SLAM Project - Long Term Ecological Study of the Impacts of Climate Change in the Natural Forest of Azores” that started in 2012, aiming to understand the impact of biodiversity erosion drivers on Azorean native forests (Azores, Macaronesia, Portugal). The data for the current study consist in an inventory of arthropods collected in three locations of a native forest fragment at Terra-Brava protected area (Terceira, Azores, Portugal) aiming to test the impact of edge effects on Azorean arthropod communities. The three locations were: (i) the edge of the forest, closer to the pastures; (ii) an intermediate area (100 m from edge); and (iii) the deepest part of the native forest fragment (more than 300 m from edge). The study was carried out between June 2014 and December 2015. A total of nine passive flight interception SLAM (Sea, Land and Air Malaise) traps were deployed (three in each of the studied locations), during 18 consecutive months. This study provides the raw data to investigate temporal and edge effect variation for the Azorean arthropod communities. NEW INFORMATION: The collected arthropods belong to a wide diversity of taxonomic groups of Arachnida, Diplopoda, Chilopoda and Insecta classes. We collected a total of 13,516 specimens from which it was possible to identify to species level almost all specimens (13,504). These identified specimens belong to 15 orders, 58 families (plus three with only genus or family level identification) and 97 species of arthropods. A total of 35 species are considered introduced, 34 native non-endemic and 28 endemic. Additionally, a total of 10 taxa (12 specimens) were recorded at genus, family or order level. This dataset will allow researchers to test the impact of edge effect on arthropod biodiversity and to investigate seasonal changes in Azorean arthropod native forest communities.Trap acquisition and fieldwork were funded by the project Portuguese National Funds, through FCT - Fundação para a Ciência e a Tecnologia, within the project UID/BIA/00329/2013-2023. The database management and Open Access was funded by the project "MACRISK-Trait-based prediction of extinction risk and invasiveness for Northern Macaronesian arthropods" Fundacao para a Ciencia e a Tecnologia (FCT) -PTDC/BIA-CBI/0625/2021 (2022-2024). MB was supported by FCT - DL57/2016/CP1375/CT0001. NT and MTF were supported by the project LIFE-BETTLES (LIFE18 NAT/PT/000864). PAVB and RG were additionally supported by FCT-UIDP/00329/2020-2024 (Thematic Line 1-Integrated ecological assessment of environmental change on biodiversity) and MACRISK - PTDC/BIA-CBI/0625/2021, through the FCT - Fundacao para a Ciencia e a Tecnologia.info:eu-repo/semantics/publishedVersio

SLAM Project - Long Term Ecological Study of the Impacts of Climate Change in the Natural Forest of Azores: II - A survey of exotic arthropods in disturbed forest habitats

BACKGROUND: The data we present consist of an inventory of exotic arthropods, potentially invasive, collected in exotic and mixed forests and disturbed native forest patches of the Azores Archipelago. The study was carried out between 2019 and 2020 in four islands: Corvo, Flores, Terceira and Santa Maria, where a total of 45 passive flight interception SLAM traps were deployed, during three to six consecutive months. This manuscript is the second contribution of the “SLAM Project - Long Term Ecological Study of the Impacts of Climate Change in the Natural Forest of Azores”. NEW INFORMATION: We provide an inventory of terrestrial arthropods belonging to Arachnida, Diplopoda, Chilopoda and Insecta classes from four Azorean islands. We identified a total of 21,175 specimens, belonging to 20 orders, 93 families and 249 species of arthropods. A total of 125 species are considered introduced, 89 native non-endemic and 35 endemic. We registered 34 new records (nine for Corvo, three for Flores, six for Terceira and 16 for Santa Maria), of which five are new for Azores, being all exotic possibly recently introduced: Dieckmanniellus nitidulus (Gyllenhal, 1838), Gronops fasciatus Küster, 1851, Hadroplontus trimaculatus (Fabricius, 1775), Hypurus bertrandi (Perris, 1852) (all Coleoptera, Curculionidae) and Cardiocondyla mauritanica Forel, 1890 (Hymenoptera, Formicidae). This publication highlights the importance of planted forests and disturbed native forest patches as reservoirs of potentially invasive arthropods and refuges for some rare relict endemic arthropod species.Trap acquisition and fieldwork were funded by the projects: Portuguese National Funds, through FCT – Fundação para a Ciência e a Tecnologia, within the project UID/BIA/ 00329/2013-2023; Direcção Regional do Ambiente - PRIBES (LIFE17 IPE/PT/000010) (2019-2020); Direcção Regional do Ambiente – LIFE-BETTLES (LIFE18 NAT_PT_000864) (2020-2024); AZORESBIOPORTAL – PORBIOTA (ACORES-01-0145-FEDER-000072) (2019-2022). The database management and Open Access was funded by the project “MACRISK-Traitbased prediction of extinction risk and invasiveness for Northern Macaronesian arthropods” Fundação para a Ciência e a Tecnologia (FCT) - PTDC/BIA-CBI/0625/2021 (2022-2024). MB was supported by FCT - DL57/2016/CP1375/CT0001. NT and MTF were supported by the project LIFE-BETTLES (LIFE18 NAT_PT_000864).info:eu-repo/semantics/publishedVersio

Standardised inventories of spiders (Arachnida, Araneae) on touristic trails of the native forests of the Azores (Portugal)

Background The sharp increase in tourist visitation of the Azores Archipelago from 2015 onwards raised concerns about the impacts of recreational tourism on native habitats. In response, a project was financed by the Azorean Government to investigate the drivers of biodiversity erosion associated with recreational tourism. Here, we present the data on spider biodiversity found on trails located within the native Azorean forests as they are home to several endemic species of great conservation value. We applied an optimised and standardised sampling protocol (COBRA) in twenty-three plots located in five trails on Terceira and Sao Miguel Islands and assessed diversity and abundance of spider species at different distances from the trail head and the trail itself. New information Of the 45 species (12435 specimens) collected, 13 were endemic to the Azores (9690 specimens), 10 native non-endemic (2047 specimens) and 22 introduced (698 specimens). This database will be the baseline of a long-term monitoring project for the assessment of touristic impacts on native forest trails. This methodology can also be used on other habitats and biogeograhical regions.Peer reviewe

SLAM Project - Long Term Ecological Study of the Impacts of Climate Change in the natural forests of Azores: V - New records of terrestrial arthropods after ten years of SLAM sampling

BACKGROUND: A long-term study monitoring arthropods (Arthropoda) is being conducted since 2012 in the forests of Azorean Islands. Named "SLAM - Long Term Ecological Study of the Impacts of Climate Change in the natural forest of Azores", this project aims to understand the impact of biodiversity erosion drivers in the distribution, abundance and diversity of Azorean arthropods. The current dataset represents arthropods that have been recorded using a total of 42 passive SLAM traps (Sea, Land and Air Malaise) deployed in native, mixed and exotic forest fragments in seven Azorean Islands (Flores, Faial, Pico, Graciosa, Terceira, São Miguel and Santa Maria). This manuscript is the fifth data-paper contribution, based on data from this long-term monitoring project. NEW INFORMATION: We targeted taxa for species identification belonging to Arachnida (excluding Acari), Chilopoda, Diplopoda, Hexapoda (excluding Collembola, Lepidoptera, Diptera and Hymenoptera (but including only Formicidae)). Specimens were sampled over seven Azorean Islands during the 2012-2021 period. Spiders (Araneae) data from Pico and Terceira Islands are not included since they have been already published elsewhere (Costa and Borges 2021, Lhoumeau et al. 2022). We collected a total of 176007 specimens, of which 168565 (95.7%) were identified to the species or subspecies level. For Araneae and some Hemiptera species, juveniles are also included in this paper, since the low diversity in the Azores allows a relatively precise species-level identification of this life-stage. We recorded a total of 316 named species and subspecies, belonging to 25 orders, 106 families and 260 genera. The ten most abundant species were mostly endemic or native non-endemic (one Opiliones, one Archaeognatha and seven Hemiptera) and only one exotic species, the Julida Ommatoiulus moreleti (Lucas, 1860). These ten species represent 107330 individuals (60%) of all sampled specimens and can be considered as the dominant species in the Azorean native forests for the target studied taxa. The Hemiptera were the most abundant taxa, with 90127 (50.4%) specimens. The Coleoptera were the most diverse with 30 (28.6%) families. We registered 72 new records for many of the islands (two for Flores, eight for Faial, 24 for Graciosa, 23 for Pico, eight for Terceira, three for São Miguel and four for Santa Maria). These records represent 58 species. None of them is new to the Azores Archipelago. Most of the new records are introduced species, all still with low abundance on the studied islands. This publication contributes to increasing the baseline information for future long-term comparisons of the arthropods of the studied sites and the knowledge of the arthropod fauna of the native forests of the Azores, in terms of species abundance, distribution and diversity throughout seasons and years.AMCS is supported by the Ramón y Cajal program (RYC2020-029407-I), financed by the Spanish Ministerio de Ciencia e Innovación. IRA and MB were funded by Portuguese funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the Norma Transitória – DL 57/2016/CP1375/CT0003 and DL 57/2016/CP1375/CT0001, respectively. Several projects supported the acquisition of traps during the last ten years, namely: EUFCT-NETBIOME –ISLANDBIODIV grant 0003/2011 (between 2012 and 2015); Portuguese National Funds, through FCT – Fundação para a Ciência e Tecnologia, within the project UID/BIA/00329/2013-2020; Direcção Regional do Ambiente - PRIBES (LIFE17 IPE/PT/ 000010) (2019); Direcção Regional do Ambiente – LIFE-BETTLES (LIFE18 NAT_PT_000864) (2020); AZORESBIOPORTAL – PORBIOTA (ACORES-01-0145- FEDER-000072) (2019); (FCT) - MACRISK-Trait-based prediction of extinction risk and invasiveness for Northern Macaronesian arthropods (FCT-PTDC/BIA-CBI/0625/2021) (2021-2022). Data curation and open Access of this manuscript were supported by the project MACRISK-Trait-based prediction of extinction risk and invasiveness for Northern Macaronesian arthropods (FCT-PTDC/BIA-CBI/0625/2021).info:eu-repo/semantics/publishedVersio

Desafios e avanços na personalização diagnóstica e terapêutica na era da inteligência artificial na saúde

The integration of artificial intelligence (AI) and machine learning (ML) in medicine represents a rapidly growing field, promising significant advances in diagnostic and treatment processes. Given this scenario, this integrative review seeks to consolidate and critically analyze the available scientific evidence on the application of these innovative technologies in medical practice. The methodology adopted for this integrative review involved a comprehensive search of the main databases, such as PubMed, Scielo and ScienceDirect, using the relevant descriptors, such as "Artificial Intelligence", "Machine Learning", "Clinical Diagnosis", "Machine Learning" and "Deep Learning". The careful selection of references included relevant studies that address the application of AI and ML in various domains of medicine, with a special focus on the references indicated in Vancouver in this abstract. The results of this review reveal a wide range of successful applications of AI and AM in medical diagnosis and treatment. Studies such as Wang et al. (2019) highlight the progress and challenges of using deep learning in medicine, while work by Erickson et al. (2017) highlights the effectiveness of ML in medical imaging, contributing to advances in clinical practice. Ethical approaches and future impacts on the actions of healthcare professionals, as discussed by Ahuja (2019) and Farhud and Zokaei (2021), emerge as crucial points in the integration of these technologies. The conclusion of this integrative review reinforces the significant transformation provided by the integration of AI and AM in medicine, offering faster and more accurate diagnoses, as well as outlining intrinsic ethical challenges. Patient privacy and ethical considerations become critical factors in this scenario. This comprehensive analysis highlights the continued need for responsible research and development, promoting advances that optimize clinical efficacy and ensure the trust of healthcare professionals and patients in the face of these transformative innovations.A integração de inteligência artificial (IA) e aprendizado de máquina (AM) na medicina representa um campo em rápido crescimento, prometendo avanços significativos nos processos de diagnóstico e tratamento. Diante desse cenário, a presente revisão integrativa busca consolidar e analisar criticamente as evidências científicas disponíveis sobre a aplicação dessas tecnologias inovadoras na prática médica. A metodologia adotada para esta revisão integrativa envolveu uma busca abrangente nas principais bases de dados, como PubMed, Scielo e Scopus, utilizando os descritores pertinentes, tais como "Inteligência Artificial", "Aprendizado de Máquina", "Diagnóstico Clínico", "Machine Learning" e "Deep Learning". A seleção criteriosa das referências incluiu estudos relevantes que abordam a aplicação de IA e AM em diversos domínios da medicina, com foco especial nas referências indicadas em Vancouver neste resumo. Os resultados desta revisão revelam uma ampla gama de aplicações bem-sucedidas de IA e AM em diagnósticos e tratamentos médicos. Estudos como o de Wang et al. (2019) destacam os progressos e desafios do uso de deep learning na medicina, enquanto trabalhos de Erickson et al. (2017) evidenciam a eficácia do AM em imagens médicas, contribuindo para avanços na prática clínica. Abordagens éticas e impactos futuros na atuação dos profissionais de saúde, conforme discutido por Ahuja (2019) e Farhud e Zokaei (2021), emergem como pontos cruciais na integração dessas tecnologias. A conclusão desta revisão integrativa reforça a transformação significativa proporcionada pela integração de IA e AM na medicina, oferecendo diagnósticos mais rápidos e precisos, bem como delineando desafios éticos intrínsecos. A privacidade do paciente e as considerações éticas tornam-se fatores críticos nesse cenário. Esta análise abrangente destaca a necessidade contínua de pesquisa e desenvolvimento responsável, promovendo avanços que otimizem a eficácia clínica e garantam a confiança dos profissionais de saúde e dos pacientes diante dessas inovações transformadoras

Future mmVLBI Research with ALMA: A European vision

Very long baseline interferometry at millimetre/submillimetre wavelengths (mmVLBI) offers the highest achievable spatial resolution at any wavelength in astronomy. The anticipated inclusion of ALMA as a phased array into a global VLBI network will bring unprecedented sensitivity and a transformational leap in capabilities for mmVLBI. Building on years of pioneering efforts in the US and Europe the ongoing ALMA Phasing Project (APP), a US-led international collaboration with MPIfR-led European contributions, is expected to deliver a beamformer and VLBI capability to ALMA by the end of 2014 (APP: Fish et al. 2013, arXiv:1309.3519). This report focuses on the future use of mmVLBI by the international users community from a European viewpoint. Firstly, it highlights the intense science interest in Europe in future mmVLBI observations as compiled from the responses to a general call to the European community for future research projects. A wide range of research is presented that includes, amongst others: - Imaging the event horizon of the black hole at the centre of the Galaxy - Testing the theory of General Relativity an/or searching for alternative theories - Studying the origin of AGN jets and jet formation - Cosmological evolution of galaxies and BHs, AGN feedback - Masers in the Milky Way (in stars and star-forming regions) - Extragalactic emission lines and astro-chemistry - Redshifted absorption lines in distant galaxies and study of the ISM and circumnuclear gas - Pulsars, neutron stars, X-ray binaries - Testing cosmology - Testing fundamental physical constantsComment: Replaced figures 2 and 3: corrected position SRT. Corrected minor typo in 5.

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages