Motivation: Progression independent of relapse activity (PIRA) is the most frequent manifestation of disability accumulation in multiple sclerosis (MS), but the mechanisms leading to PIRA are currently unknown. Goal(s): To investigate the link between PIRA and white matter degeneration in people with MS. Approach: To compare the integrity of normal-appearing white matter (NAWM) between patients with MS who experienced PIRA versus stable patients using diffusion tensor imaging (DTI) measures from a clinical-compatible protocol. Results: Patients with PIRA exhibited significant differences in DTI-derived measures compared to stable patients: reduced fractional anisotropy and increased mean and radial diffusivity in NAWM. Impact: This study sheds light on the relationship between progression independent of relapse activity (PIRA) and white matter degeneration in people with multiple sclerosis. The results have important implications for understanding the mechanisms of disability progression in relapsing-remitting multiple sclerosis.

Motivation: Quantitative MRI (qMRI) offers sensitive and specific measures to study age-related microstructural changes in the brain. However, models assessing age trajectories in qMRI brain properties are often incomparable among centers. Goal(s): Develop normative models reflecting aging trajectories and assess the impact of bi-centric, non-fully matched protocols in brain aging studies. Approach: Investigating age trajectories in cortical regions using polynomial regression models, focusing on quantitative R1, R2*, and susceptibility mapping (QSM). Results: We validated data harmonization by observing the impact on normative trajectories using bicentric data, where we noted significantly different maturation and aging inflections for R1 and R2* trajectories across cortical regions. Impact: This bi-centric, multi-parameter qMRI study investigates age-dependent variations across cortical regions, offering a valuable reference for subsequent qMRI aging research and emphasizing age effects on the cortical surface.

ASL-MRI is reported as an option to assess potentially heterogeneous physiological processes important for tumour treatment. Therefore, we explored the heterogeneity in normalised CBF as an imaging biomarker for assessment of treatment effect in pLGG. There is a noticeable effect of chemotherapy observed as a change in texture of healthy appearing brain tissue. A high difference in texture between treated and non-treated patients for non-enhancing tumour part is observed, suggesting that texture, based on co-occurrence matrices, is suitable as an imaging biomarker for assessment of treatment effect in pLGG.

After sentinel lymph nodes are detected using SPIONs and excised, their characterization is important to detect possible metastases. In this research a low-field (0.5T) tabletop MRI scanner was tested for this purpose using 4x accelerated high resolution 3D acquisition. Both simulations and experiments on excised pig lymph nodes showed promising results, with the accelerated scans showing similar image quality with respect to fully sampled datasets. This protocol shows lymph nodes can be imaged at 0.25 mm isotropic resolution within reasonable scan times. Clinical usage should be proven by scanning true metastatic lymph nodes.

APTw imaging is a potential imaging biomarker to assess treatment effects in brain tumours, especially at high field MRI (7T) due to improved signal-to-noise-ratio enabling the assessment of APTw values in heterogenous tumours. Embedding of APTw imaging in clinical decision making requires insight in the repeatability of APTw imaging. Therefore, we evaluated the repeatability of APTw imaging at 7T by using a phantom and in vivo in the human brain subjects. Repeatable and specific APTw maps were obtained at 7T, which facilitate the potential of detecting metabolic changes in brain tumours due to treatment.

What distinguishes the AGI approach from the initial, supposedly equally idealistic and holistic, AI approach? Why do we think that we could make any progress in our recent times? The answer to these questions is not clear

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors or originators and do not necessarily reflect the views of the National Science Foundation. Abstract Attacks on industrial control systems remain rare overall, yet they may carefully target their victims. A particularly challenging threat consists of adversaries aiming to change a plant's *process flow*. A prominent example of such a threat is Stuxnet, which manipulated the speed of centrifuges to operate outside of their permitted range. Existing intrusion detection approaches fail to address this type of threat. In this paper we propose a novel network monitoring approach that takes process semantics into account by (1) extracting the value of process variables from network traffic, (2) characterizing types of variables based on the behavior of time series, and (3) modeling and monitoring the regularity of variable values over time. We implement a prototype system and evaluate it with real‐world network traffic from two operational water treatment plants. Our approach is a first step towards devising intrusion detection systems that can detect semantic attacks targeting to tamper with a plant's physical processes.