Estudo da vegetação arbórea e arbustiva adequada a projetos de engenharia natural em Portugal

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia - ULA Engenharia Natural (EN) usa as raízes das plantas no reforço e proteção dos solos, aproveitando os seus recursos endógenos, utilizando uma energia renovável e promovendo a aceleração da sucessão ecológica. Esta técnica, apesar de pouco divulgada em Portugal, demonstra ser uma boa alternativa ao uso do betão na retenção de solo em taludes e encostas, áreas que se caraterizam pela existência de fatores edáficos e de humidade limitantes. Foram estudadas árvores e arbustos da flora de Portugal continental no sentido de se obter um conjunto de espécies, ainda não utilizadas em projetos de EN, com suposta capacidade de propagação vegetativa. Foram selecionadas como espécies candidatas a submeterem-se a ensaios, aquelas que apresentaram ampla distribuição geográfica, demonstrando, aparentemente, maior utilidade em todo o território de Portugal continental. Os resultados quantitativos do enraizamento adventício e das características biotécnicas dos sistemas radiculares das quatro espécies testadas, Fraxinus angustifolia Vahl, Sambucus nigra L., Rosmarinus officinalis L., Viburnum tinus L., permitem confirmar que todas as quatro espécies se adaptam aos projetos de EN, e ainda, que existe uma relação positiva entre o desenvolvimento da parte aérea e da parte hipogeia das plantas, com particular destaque para as novas raíze

Selection of suitable species for bioengineering

Soil bioengineering was developed in central Europe after World War II and in Mediterranean Europe in the last twenty years; soil bioengineering has been recently applied also in Portugal suggesting its potential future development. Soil bioengineering, to consolidate and stabilize sliding down slopes, uses indigenous trees and shrubs with good vegetative propagation. A key feature of these species, employed as cuttings or as whole plants, is to not get damaged when their stems are buried in the ground about one meter. In this article is selected, from Mainland Portugal flora, a list of plant species, starting from scientific literature of Portugal, Central Europe and Central and Southern Europe, and using practical knowledge developed in Southern Italy. Afterwards groups of species are defined as (i) appropriate for soil bioengineering works to do in Portugal and (ii) probably adequate, requiring further researches to improve the knowledge about their feature. Tests are planned to assess the biotechnical features of this second group

Erratum to "Validity of Mental Workload Measures in a Driving Simulation Environment"

n/

Glucose regulates insulin mitogenic effect by modulating SHP-2 activation and localization in JAr cells.

The glucose effect on cell growth has been investigated in the JAr human choriocarcinoma cells. When JAr cells were cultured in the presence of 6 mm glucose (LG), proliferation and thymidine incorporation were induced by serum, epidermal growth factor, and insulin-like growth factor 1 but not by insulin. In contrast, at 25 mm glucose (HG), proliferation and thymidine incorporation were stimulated by insulin, serum, epidermal growth factor, and insulin-like growth factor 1 to a comparable extent, whereas basal levels were 25% lower than those in LG. HG culturing also enhanced insulin-stimulated insulin receptor and insulin receptor substrate 1 (IRS1) tyrosine phosphorylations while decreasing basal phosphorylations. These actions of glucose were accompanied by an increase in cellular tyrosine phosphatase activity. The activity of SHP-2 in HG-treated JAr cells was 400% of that measured in LG-treated cells. SHP-2 co-precipitation with IRS1 was also increased in HG-treated cells. SHP-2 was mainly cytosolic in LG-treated cells. However, HG culturing largely redistributed SHP-2 to the internal membrane compartment, where tyrosine-phosphorylated IRS1 predominantly localizes. Further exposure to insulin rescued SHP-2 cytosolic localization, thereby preventing its interaction with IRS1. Antisense inhibition of SHP-2 reverted the effect of HG on basal and insulin-stimulated insulin receptor and IRS1 phosphorylation as well as that on thymidine incorporation. Thus, in JAr cells, glucose modulates insulin mitogenic action by modulating SHP-2 activity and intracellular localization

How SARS-CoV-2 Infection Impacts the Management of Patients with Vulvar Cancer: Experience in a Third-Level Hospital of Southern Italy

: Background: Since February 2020, the spread of Coronavirus Disease 2019 (COVID-19) in Italy has induced the government to call for lockdown of any activity apart from primary needs, and changing the lives of each of us. All that has dramatically impacted the management of patients affected by cancer. Patients with vulvar cancer (VC) represent a particularly frail population because they are elderly and affected by multiple comorbidities. The aim of this study is to evaluate the clinical impact of the SARS-CoV-2 infection on VC patients in terms of delay or impossibility of carrying out the scheduled treatment. Methods: The medical records of patients affected by vulvar tumors, referred to "DAI Materno-Infantile" of AOU Federico II of Naples between February 2020 and January 2022 were retrospectively analyzed. The presence of a positive reverse transcription-polymerase chain reaction (RT-PCR) in nasopharyngeal swab defined the positivity to SARS-CoV-2. Results: Twenty-four patients with VC were analyzed and scheduled for treatment. The median age was 70.7 years (range: 59-80). Seven (29.2%) patients were diagnosed with SARS-CoV-2 infection: In three (42.8%) patients, the treatment was delayed with no apparent consequences, in four (57.2%), the treatment was delayed or changed due to cancer progression and, of these four, one died due to respiratory complications of COVID-19, and one died due to oncologic disease progression. Conclusion: COVID-19 caused, in most cases, significant delays in oncologic treatments and high mortality in our series of patients affected by VC

Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology

Clinical trial matching is a key process in health delivery and discovery. In practice, it is plagued by overwhelming unstructured data and unscalable manual processing. In this paper, we conduct a systematic study on scaling clinical trial matching using large language models (LLMs), with oncology as the focus area. Our study is grounded in a clinical trial matching system currently in test deployment at a large U.S. health network. Initial findings are promising: out of box, cutting-edge LLMs, such as GPT-4, can already structure elaborate eligibility criteria of clinical trials and extract complex matching logic (e.g., nested AND/OR/NOT). While still far from perfect, LLMs substantially outperform prior strong baselines and may serve as a preliminary solution to help triage patient-trial candidates with humans in the loop. Our study also reveals a few significant growth areas for applying LLMs to end-to-end clinical trial matching, such as context limitation and accuracy, especially in structuring patient information from longitudinal medical records.Comment: 24 pages, 5 figures, accepted at Machine Learning for Healthcare (MLHC) 202

Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer

Causal discovery is becoming a key part in medical AI research. These methods can enhance healthcare by identifying causal links between biomarkers, demographics, treatments and outcomes. They can aid medical professionals in choosing more impactful treatments and strategies. In parallel, Large Language Models (LLMs) have shown great potential in identifying patterns and generating insights from text data. In this paper we investigate applying LLMs to the problem of determining the directionality of edges in causal discovery. Specifically, we test our approach on a deidentified set of Non Small Cell Lung Cancer(NSCLC) patients that have both electronic health record and genomic panel data. Graphs are validated using Bayesian Dirichlet estimators using tabular data. Our result shows that LLMs can accurately predict the directionality of edges in causal graphs, outperforming existing state-of-the-art methods. These findings suggests that LLMs can play a significant role in advancing causal discovery and help us better understand complex systems