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In the field of neuroscience as well as in the clinical setting, the neurotransmitter dopamine (DA) is an analyte which is important for research as well as medical purposes. There are plenty of methods available to measure dopamine quantitatively, with voltammetric ones such as differential pulse voltammetry (DPV) being among the most convenient and simple ones. However, dopamine often occurs, either naturally or because of the requirements of involved enzymatic systems, alongside substances that can influence the signal it produces upon electrochemical conversion. An example for such substances is the magnesium ion, which itself is not electrochemically active in the potential range needed for DA oxidation, but influences the dopamine signal. We have characterized the properties of DPV signals subject to the interaction between DA and Mg2+ and show that, although these properties are changing in a nonlinear fashion when both concentrations are varying, relatively simple linear mathematical models can be used to determine dopamine concentrations quantitatively in the presence of magnesium ions. The focus of this study is thus, the mathematical treatment of experimental data in order to overcome an analytical problem and not the investigation of the chemical background of DA-Mg2+ interaction.
Transparenz und Erklärbarkeit sind essenzielle Faktoren bei der Entwicklung sicherer KI-Modelle für den Fußgängerschutz. Dies ermöglicht nicht nur eine verbesserte Akzeptanz, sondern fördert auch das Vertrauen der Nutzer in die präventiven Fähigkeiten dieser Systeme.
Die Integration von Sensoren, maschinellem Lernen und fortschrittlichen Algorithmen ermöglicht eine präzise Analyse von Fußgängerverhalten und Umgebungsfaktoren. Dabei spielt die Echtzeitverarbeitung eine entscheidende Rolle, um schnell auf sich ändernde Situationen zu reagieren.
Zusätzlich zur Technologieentwicklung betont diese Analyse die Notwendigkeit strikter Sicherheitsmaßnahmen, um potenzielle Risiken und Sicherheitslücken zu minimieren. Dazu gehören Sicherheitsprotokolle für die Datenspeicherung, den Modellschutz und den Zu-griff auf KI-Systeme.
Die Einhaltung gesetzlicher und ethischer Richtlinien ist ein weiterer wichtiger Aspekt. Regulatorische Vorgaben und ethische Standards sollen sicherstellen, dass KI-Technologien im Automobilsektor nicht nur sicher, sondern auch im Einklang mit gesellschaftlichen Werten und Normen eingesetzt werden.
In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into “Sustainability of AI” and “AI for Sustainability”, revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field’s maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations’ Sustainable Development Goals (SDGs), and addressing stakeholders’ influence.
Despite the progress of Artificial Intelligence (AI) and its contribution to the advancement of human society, the prioritization of ethical principles from the viewpoint of its users has not yet received much attention and empirical investigations. This is important to develop appropriate safeguards and increase the acceptance of AI-mediated technologies among all members of society. In this research, we collected, integrated, and prioritized ethical principles for AI systems with respect to their relevance in different real-life application scenarios. First, an overview of ethical principles for AI was systematically derived from various academic and non-academic sources. Our results clearly show that transparency, justice and fairness, non-maleficence, responsibility, and privacy are most frequently mentioned in this corpus of documents. Next, an empirical survey to systematically identify users’ priorities was designed and conducted in the context of selected scenarios: AI-mediated recruitment (human resources), predictive policing, autonomous vehicles, and hospital robots. We anticipate that the resulting ranking can serve as a valuable basis for formulating requirements for AI-mediated solutions and creating AI algorithms that prioritize user’s needs. Our target audience includes everyone who will be affected by AI systems, e.g., policy makers, algorithm developers, and system managers as our ranking clearly depicts user’s awareness regarding AI ethics.
The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
(2023)
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
ChatGPT is a fascinating AI text generator tool. It is a language model developed by OpenAI, a research and deployment company with the mission, according to OpenAI’s website: “to ensure that artificial general intelligence benefits all of humanity”. ChatGPT is able to generate human-like texts. But how does it work? What about the quality of the texts it provides? And is it capable of being self-reflective? Information sources must be efficient, effective and reliable in education, in order to enhance students’ learning process. For this reason, we started a dialogue with ChatGPT-3 while using, among others, a SWOT analysis it generated about its own functioning in an educational setting. This enabled us, as human authors, to analyze the extent to which this AI system is able to practice self-reflection. Finally, the paper sketches implications for education and future research.
The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients’ outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan–Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1.
Most research on fairness in Machine Learning assumes the relationship between fairness and accuracy to be a trade-off, with an increase in fairness leading to an unavoidable loss of accuracy. In this study, several approaches for fair Machine Learning are studied to experimentally analyze the relationship between accuracy and group fairness. The results indicated that group fairness and accuracy may even benefit each other, which emphasizes the importance of selecting appropriate measures for performance evaluation. This work provides a foundation for further studies on the adequate objectives of Machine Learning in the context of fair automated decision making.
Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.