Unlocking AI’s Potential

Rapid advances in artificial intelligence (AI) have fueled high expectations for the technology’s potential to fundamentally transform our economy and society through automation. However, given the inscrutability and, sometimes, susceptibility to error of AI systems, we argue that the focus should shift towards fostering effective human-AI collaboration rather than pursuing automation alone. In this context, system decisions must be made available to decision-makers in an explainable and understandable manner, as further required by the EU’s recently passed AI Act. Research shows that there is potential for humans to learn from explainable AI systems and improve their own performance over time. Meanwhile, in addition to enabling humans to benefit from working with AI systems on various everyday tasks, such collaboration ensures the safe and reliable use of AI systems, especially in high-risk areas such as medicine, where human oversight remains paramount

Infrared attenuated total reflection spectroscopy for monitoring biological systems

Mid-infrared (MIR) spectroscopy has been recognized as an important analytical technique, and is widely applied for qualitative and quantitative analysis of materials with an increasing interest in addressing complex organic or biologic constituents. In the presented thesis, (a) the fundamental principles for IR spectroscopic applications via in vivo catheters in combination with multivariate data analysis technique were developed, and (b) the combination with a second analytical technique ¨C scanning electrochemical microscopy (SECM) - for enhancing the information obtained at complex or frequently changing matrices was demonstrated. The first part of this thesis focused on the combination of different MIR measurment techniques with specific focus on evanescent field absorption spectroscopy along with multivariate data analysis methods, for the discrimination of atherosclerotic and normal rabbit aorta tissues. Atherosclerotic and normal rabbit aorta tissues are characterized by marked differences in chemical composition governed by their water, lipid, and protein content. Strongly overlapping infrared absorption features of the different constituents and the complexity of the tissue matrix render the direct evaluation of molecular spectroscopic characteristics obtained from IR measurements challenging for classification. We have successfully applied multivariate data analysis and classification techniques based on principal component analysis (PCA), partial least squares regression (PLS), and linear discriminant analysis (LDA) to IR spectroscopic data obtained by infrared attenuated total reflectance (IR-ATR) measurements, reflection IR microscopy, and a recently developed IR-ATR catheter prototype for future in vivo diagnostic applications. Training and test data were collected ex vivo at atherosclerotic and normal rabbit aorta samples. The successful classification results at atherosclerotic and normal aorta samples utilizing the developed data evaluation routines reveals the potential of IR spectroscopy combined with multivariate classification strategies for in vitro, and ¨C in future - in vivo applications. The second part of this thesis aimed at the development of a novel multifunctional analytical platform by combining SECM with single-bounce IR-ATR spectroscopy for in situ studies of electrochemically active or electrochemically induced processes at the IR waveguide surface via simultaneous evanescent field absorption spectroscopy. The utility of the developed SECM-IR-ATR platform was demonstrated by spectroscopically monitoring microstructured polymer depositions induced via feedback mode SECM experiments using a 25μm Pt disk ultramicroelectrode (UME). The surface of a ZnSe ATR crystal was coated with a thin layer of 2,5-di-(2-thienyl)-pyrrole (SNS), which was then polymerized in a Ru(bpy) ₃ ² ⁺-mediated feedback mode SECM experiment. The polymerization reaction was simultaneously spectroscopically monitored by recording the absorption intensity changes of specific IR bands characteristic for SNS, thereby providing information on the polymerization progress, mechanism, and level of surface modification. Furthermore, a novel current-independent approach mechanism for positioning the UME in aqueous electrolyte solution was demonstrated by monitoring IR absorption changes of borosilicate glass (BSG) shielding the UME, and of water within the penetration depth of the evanescent field.Ph.D.Committee Chair: Mizaikoff, Boris; Committee Member: Fernandez, Facundo; Committee Member: Orlando, Thomas; Committee Member: Palmer. Richard; Committee Member: Whetten, Rober

The Clinical Utility of Food Addiction and Eating Addiction

This book is a collection of manuscripts on breast reconstruction, the topic of a Special Issue of Medicina Journal. The book begins with a review of the literature on the most recent reconstructive strategies using biological dermal matrices and moves toward the management of pain and infections. Some aspects of regenerative surgery are also clarified and an analysis focuses on social disparities in access to breast reconstruction. The final part of this book is dedicated to nipple–areola reconstruction, the last surgical step of breast reconstruction

Convolutional Neural Network in Pattern Recognition

Since convolutional neural network (CNN) was first implemented by Yann LeCun et al. in 1989, CNN and its variants have been widely implemented to numerous topics of pattern recognition, and have been considered as the most crucial techniques in the field of artificial intelligence and computer vision. This dissertation not only demonstrates the implementation aspect of CNN, but also lays emphasis on the methodology of neural network (NN) based classifier. As known to many, one general pipeline of NN-based classifier can be recognized as three stages: pre-processing, inference by models, and post-processing. To demonstrate the importance of pre-processing techniques, this dissertation presents how to model actual problems in medical pattern recognition and image processing by introducing conceptual abstraction and fuzzification. In particular, a transformer on the basis of self-attention mechanism, namely beat-rhythm transformer, greatly benefits from correct R-peak detection results and conceptual fuzzification. Recently proposed self-attention mechanism has been proven to be the top performer in the fields of computer vision and natural language processing. In spite of the pleasant accuracy and precision it has gained, it usually consumes huge computational resources to perform self-attention. Therefore, realtime global attention network is proposed to make a better trade-off between efficiency and performance for the task of image segmentation. To illustrate more on the stage of inference, we also propose models to detect polyps via Faster R-CNN - one of the most popular CNN-based 2D detectors, as well as a 3D object detection pipeline for regressing 3D bounding boxes from LiDAR points and stereo image pairs powered by CNN. The goal for post-processing stage is to refine artifacts inferred by models. For the semantic segmentation task, the dilated continuous random field is proposed to be better fitted to CNN-based models than the widely implemented fully-connected continuous random field. Proposed approaches can be further integrated into a reinforcement learning architecture for robotics

The History and Practice of College Health

This volume is the first definitive reference and textbook in the one-hundred-fifty year history of college health. Written for professionals and for those working in student services and higher education administration, it covers the history of college health, administrative matters including financing and accreditation, and clinical issues such as women’s health, HIV/AIDS, and mental health. The book also focuses on prevention, including immunization and tuberculin testing. The contributors are well respected in the field and are actively working in the specific areas on which they write. H. Spencer Turner, MD, is director of the University Health Service and clinical professor of preventative medicine and environmental health at the University of Kentucky. Janet L. Hurley, Ph.D., is the Associate Director and Administrator of the University of Kentucky\u27s Health Service.https://uknowledge.uky.edu/upk_history_of_science_technology_and_medicine/1003/thumbnail.jp

Computational Identification of Novel MicroRNAs Using Intrinsic RNA Folding Measures

Ph.DDOCTOR OF PHILOSOPH