Unterschiede im Interessenprofil von Studenten der Ergotherapie und Logopädie

Das primäre Ziel dieser Diplomarbeit ist es, die Aussagekraft des neu entwickelten Interessenfragebogen RIASEC-RRK, Version 2 (Arendasy et al., 2007) anhand der Stichproben der Logopäden und der Ergotherapeuten zu erweitern. Gleichzeitig werden Interessenschwerpunkte beider Studienrichtungen erarbeitet und signifikante Unterschiede in den Interessensprofilen der beiden Studienrichtungen ermittelt. Bei beiden Studienrichtungen dominieren die sozialen Interessen (S), gefolgt von den künstlerisch-sprachlichen Interessen (A) und als drittstärkstes Interesse der wissenschaftlich, forschende Bereich (I). Weiters zeigen Ergotherapie-Studenten ein signifikant höheres praktisch -technisches Interesse, Logopädie-Studenten hingegen ein signifikant höheres konventionelles Interesse. In den Dimensionen: „Investigative“, „Artistic“, „Social“ und „Enterprising“ zeigen sich keine signifikanten Unterschiede zwischen Ergotherapie- und Logopädie-Studenten. Für eine bestmögliche Entscheidung bezüglich der Studienwahl, ist es jedoch wichtig, dass in der Studienberatung neben den Ergebnissen dieser Diplomarbeit auch weitere Informationen, wie Aufgabenbereiche, Beschäftigungsmöglichkeiten und Arbeitsfelder, mit einbezogen werden.The aim of this study is to enlarge the meaningfulness of the new developed interest questionnaire RIASEC-RRK, Version 2 (Arendasy et al., 2007) with the sample of students of occupational therapy and logopedia and to investigate the differences in Holland´s (1997) interest types (realistic, investigative, artistic, social, enterprising and conventional). 110 students completed the online questionnaire which has a total of 126 questions. For both studies a three-dimensional interest structure can be specified: the social interest dominates, followed by the artistic and the investigative interest. Students of occupational therapy show higher realistic interests and the students of logopedia demonstrate higher conventional interests. This research has identified personal characteristics that can be added to current selection criteria to assist in identifying suitable candidates for occupational therapy and logopedia education

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Fear of Influenza Resurgence amid COVID-19 Pandemic: Need for Effective Flu Vaccine Still Exists

As influenza season was approaching in 2020, public health officials feared that influenza would worsen the COVID-19 situation [...

IManageCancer: developing a platform for empowering patients and strengthening self-management in cancer diseases

Cancer research has led to more cancer patients being cured, and many more enabled to live with their cancer. As such, some cancers are now considered a chronic disease, where patients and their families face the challenge to take an active role in their own care and in some cases in their treatment. To this direction the iManageCancer project aims to provide a cancer specific self-management platform designed according to the needs of patient groups while focusing, in parallel, on the wellbeing of the cancer patient. In this paper, we present the use-case requirements collected using a survey, a workshop and the analysis of three white papers and then we explain the corresponding system architecture. We describe in detail the main technological components of the designed platform, show the current status of development and we discuss further directions of research

SFN does not trigger expression of interferon-stimulated anti-viral factors SAMHD1 or MX2.

<p>PMA-differentiated THP1 cells were mock-treated or treated with media supplemented with vehicle only (DMSO) or with 10 μM SFN or with 500 U/mL of IFNα. (A), Proteins from whole cell lysates were resolved by SDS-PAGE and identified by western blotting using antibodies with the indicated specificities. (B), Densitometric analysis was performed on the Nrf2, SAMHD1, MX2, NQO1 and GCLM (NQO1 and GCLM are both indicators of Nrf2 function) bands and normalized to the values of the corresponding tubulin bands. The relative normalized intensities of the bands were then graphed.</p

SFN blocks infection after entry and but before 2-LTR circle formation.

<p>Replicate cultures of hMDMs were pretreated with vehicle (DMSO)-containing media, with 5 μM AZT or with 10 μM SFN. Twenty four hours after treatment, the samples were infected with VSV-G-pseudotyped HIV-1 encoding GFP in place of <i>nef</i>. Cultures treated with heat-inactivated virus served as controls for plasmid carry over and for impaired viral entry. Cells were harvested and DNA was isolated 24 hours after infection. Viral DNA products were detected by real-time PCR using primer sets specific for the indicated stage of reverse transcription. (A), Relative quantities of late reverse transcription products, (B), 2-LTR circles, and (C), integrated proviruses. The bar graph represents the data for replicate experiments (n = 3).</p

SFN action impacts HIV-2 as well as HIV-1 and is not reporter dependent.

<p>PMA-differentiated THP1 cells were treated with media supplemented with vehicle only (DMSO), 5 μM AZT or with 10 μM SFN. Twenty-four hours after treatment, the samples were either mock infected or infected with (A), VSV-G pseudotyped HIV-1 encoding firefly luciferase in place of <i>nef</i> or (B), VSV-G pseudotyped HIV-2 encoding firefly luciferase in place of <i>nef</i>. Twenty-four hours after infection, luciferase activity was measured by photon emission. (C), In parallel, the same experiment as in (A) and (B) was performed except that THP1 cells were infected with VSV-G-pseudotyped HIV-1 with GFP in place of <i>nef</i> or (D), VSV-G pseudotyped HIV-2 with GFP in place of <i>nef</i>. The samples with GFP-reporter viruses were fixed and harvested 24 h after infection and the fraction of GFP(+) cells was enumerated by flow cytometry. Bar graphs represent the data for replicate experiments (n = 3).</p