8,953 research outputs found
Economia colaborativa
A importância de se proceder à análise dos principais desafios jurídicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutível, correspondendo à necessidade de se fomentar a segurança jurídica destas práticas, potenciadoras de crescimento económico e bem-estar social.
O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domínio, com o objetivo de identificar os problemas jurídicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação.
O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202
Identifizierung prädiktiver und prognostischer Biomarker in unterschiedlichen Tumorkompartimenten des ösophagealen Adenokarzinoms
Das ösophageale Adenokarzinom zeigt eine global steigende Inzidenz und hat mit einer 5-Jahres-Überlebensrate von weniger als 25% eine schlechte Prognose. Personalisierte Therapieansätze sind selten und prognostische/prädiktive Biomarker des Tumormikromilieus sind unzureichend charakterisiert. Die kumulative Promotion nähert sich dieser Problematik in drei unterschiedlichen Schwerpunkten. 1. Zur Identifizierung Kompartiment-spezifischer Biomarker wurde eine Methode entwickelt, welche als kostengünstige Alternative zum sc-Seq Expressionsprofile individueller Zelltypen generiert. Dabei erfolgt die Extraktion der RNA nicht aus Einzelzellen, sondern aus flowzytometrisch-getrennten Zellkompartimenten. Die Separation der Proben in Epithelzellen, Immunzellen und Fibroblasten wurde durch verschiedene Verfahren validiert und eine suffiziente Ausbeute an RNA auch für kleine Gewebemengen gezeigt. 2. Biomarker des Immunzellkompartiments als therapeutische Angriffspunkte wurden in einem Patientenkollektiv von bis zu 551 Patienten auf ihre Bedeutung beim EAC überprüft. Es zeigte sich eine Expression der Immuncheckpoints LAG3, VISTA und IDO auf TILs durch IHC und RNA-Sonden basierte Verfahren in einem relevanten Anteil (LAG3: 11,4%, VISTA: 29%, IDO: 52,6%). Es konnte eine prognostisch günstige Bedeutung der VISTA, LAG3 und IDO Expression gezeigt werden. Durch den Vergleich von Genexpressionsprofilen aus therapienaiven und vorbehandelten Tumoren konnte zudem ein immunsuppressiver Effekt von neoadjuvanten Therapiekonzepten auf das Tumormikromilieu des EACs gezeigt werden. Dabei kam es zur verminderten Expression von Checkpoints und Anzahl TILs nach (Radio-) Chemotherapie. 3. Im Tumorzellkompartiment wurde die Rolle von Amplifikationen in ErbB-Rezeptor abhängigen Signalwegen durch FISH-Technik und Immunhistochemie evaluiert. Es fanden sich KRAS Amplifikationen in 17,1%, PIK3CA Amplifikationen in 5% sowie eine HER2/neu-Überexpression in 14,9% der untersuchten Tumore
A scoping review of natural language processing of radiology reports in breast cancer
Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing
A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets
The visual modeling method enables flexible interactions with rich graphical
depictions of data and supports the exploration of the complexities of
epidemiological analysis. However, most epidemiology visualizations do not
support the combined analysis of objective factors that might influence the
transmission situation, resulting in a lack of quantitative and qualitative
evidence. To address this issue, we have developed a portrait-based visual
modeling method called +msRNAer. This method considers the spatiotemporal
features of virus transmission patterns and the multidimensional features of
objective risk factors in communities, enabling portrait-based exploration and
comparison in epidemiological analysis. We applied +msRNAer to aggregate
COVID-19-related datasets in New South Wales, Australia, which combined
COVID-19 case number trends, geo-information, intervention events, and
expert-supervised risk factors extracted from LGA-based censuses. We perfected
the +msRNAer workflow with collaborative views and evaluated its feasibility,
effectiveness, and usefulness through one user study and three subject-driven
case studies. Positive feedback from experts indicates that +msRNAer provides a
general understanding of analyzing comprehension that not only compares
relationships between cases in time-varying and risk factors through portraits
but also supports navigation in fundamental geographical, timeline, and other
factor comparisons. By adopting interactions, experts discovered functional and
practical implications for potential patterns of long-standing community
factors against the vulnerability faced by the pandemic. Experts confirmed that
+msRNAer is expected to deliver visual modeling benefits with spatiotemporal
and multidimensional features in other epidemiological analysis scenarios
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
Cardiovascular diseases prediction by machine learning incorporation with deep learning
It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures
A Survey on Biomedical Text Summarization with Pre-trained Language Model
The exponential growth of biomedical texts such as biomedical literature and
electronic health records (EHRs), provides a big challenge for clinicians and
researchers to access clinical information efficiently. To address the problem,
biomedical text summarization has been proposed to support clinical information
retrieval and management, aiming at generating concise summaries that distill
key information from single or multiple biomedical documents. In recent years,
pre-trained language models (PLMs) have been the de facto standard of various
natural language processing tasks in the general domain. Most recently, PLMs
have been further investigated in the biomedical field and brought new insights
into the biomedical text summarization task. In this paper, we systematically
summarize recent advances that explore PLMs for biomedical text summarization,
to help understand recent progress, challenges, and future directions. We
categorize PLMs-based approaches according to how they utilize PLMs and what
PLMs they use. We then review available datasets, recent approaches and
evaluation metrics of the task. We finally discuss existing challenges and
promising future directions. To facilitate the research community, we line up
open resources including available datasets, recent approaches, codes,
evaluation metrics, and the leaderboard in a public project:
https://github.com/KenZLuo/Biomedical-Text-Summarization-Survey/tree/master.Comment: 19 pages, 6 figures, TKDE under revie
Message Journal, Issue 5: COVID-19 SPECIAL ISSUE Capturing visual insights, thoughts and reflections on 2020/21 and beyond...
If there is a theme running through the Message Covid-19 special issue, it is one of caring. Of our own and others’ resilience and wellbeing, of friendship and community, of students, practitioners and their futures, of social justice, equality and of doing the right thing. The veins of designing with care run through the edition, wide and deep. It captures, not designers as heroes, but those with humble views, exposing the need to understand a diversity of perspectives when trying to comprehend the complexity that Covid-19 continues to generate.
As graphic designers, illustrators and visual communicators, contributors have created, documented, written, visualised, reflected, shared, connected and co-created, designed for good causes and re-defined what it is to be a student, an academic and a designer during the pandemic. This poignant period in time has driven us, through isolation, towards new rules of living, and new ways of working; to see and map the world in a different light. A light that is uncertain, disjointed, and constantly being redefined.
This Message issue captures responses from the graphic communication design community in their raw state, to allow contributors to communicate their experiences through both their written and visual voice. Thus, the reader can discern as much from the words as the design and visualisations.
Through this issue a substantial number of contributions have focused on personal reflection, isolation, fear, anxiety and wellbeing, as well as reaching out to community, making connections and collaborating. This was not surprising in a world in which connection with others has often been remote, and where ‘normal’ social structures of support and care have been broken down. We also gain insight into those who are using graphic communication design to inspire and capture new ways of teaching and learning, developing themselves as designers, educators, and activists, responding to social justice and to do good; gaining greater insight into society, government actions and conspiracy. Introduction: Victoria Squire - Coping with Covid: Community, connection and collaboration: James Alexander & Carole Evans, Meg Davies, Matthew Frame, Chae Ho Lee, Alma Hoffmann, Holly K. Kaufman-Hill, Joshua Korenblat, Warren Lehrer, Christine Lhowe, Sara Nesteruk, Cat Normoyle & Jessica Teague, Kyuha Shim. - Coping with Covid: Isolation, wellbeing and hope: Sadia Abdisalam, Tom Ayling, Jessica Barness, Megan Culliford, Stephanie Cunningham, Sofija Gvozdeva, Hedzlynn Kamaruzzaman, Merle Karp, Erica V. P. Lewis, Kelly Salchow Macarthur, Steven McCarthy, Shelly Mayers, Elizabeth Shefrin, Angelica Sibrian, David Smart, Ane Thon Knutsen, Isobel Thomas, Darryl Westley. - Coping with Covid: Pedagogy, teaching and learning: Bernard J Canniffe, Subir Dey, Aaron Ganci, Elizabeth Herrmann, John Kilburn, Paul Nini, Emily Osborne, Gianni Sinni & Irene Sgarro, Dave Wood, Helena Gregory, Colin Raeburn & Jackie Malcolm. - Coping with Covid: Social justice, activism and doing good: Class Action Collective, Xinyi Li, Matt Soar, Junie Tang, Lisa Winstanley. - Coping with Covid: Society, control and conspiracy: Diana Bîrhală, Maria Borțoi, Patti Capaldi, Tânia A. Cardoso, Peter Gibbons, Bianca Milea, Rebecca Tegtmeyer, Danne Wo
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