Bayesian optimization framework for data-driven materials design

The improvement of experimental design and the optimization of materials’properties with complex and partially unknown behaviors are common problems in material science. In the context of aqueous foams, the microstructure has a major influence on the properties of the resulting foam. Multiple interlinked parameters yield a large design space that requires tuning to tailor the microstructure evolution and resulting physical qualities. Our goal is a data-driven framework that uses machine learning to guide both experiments and simulations in an autonomous closed-loop. This iterative approach presents a valuable opportunity to accelerate materials development processes. A design of experiments methodology utilizing Bayesian Optimization is used to efficiently explore and exploit the search space, while minimizing the number of required evaluations. This approach allows to select the next most informative evaluation to perform, autonomously and adaptively learning from the already acquired data. The designed workflow is implemented into the data platform Kadi4Mat1, which provides the possibility of storing heterogeneous provenance data, along with a common workspace to integrate analysis methods and visualization. Our contribution within Kadi4Mat strongly relies on the reuse of data, and it is an example of the close interoperability between experimental and simulation research that the platform supports, in full alignment with the FAIR principles. Acknowledgements: This work is funded by the Ministry of Science, Research and Art Baden-Württemberg (MWK-BW) in the project MoMaF–Science Data Center, with funds from the state digitization strategy digital@bw (project number 57)

Successful surgical treatment of a giant coronary artery aneurysm presenting with recurrent profuse haemoptysis

We present the case of successful resection of a giant aneurysm of the LAD presenting with recurrent severe haemoptysis in a 72-year old man. He was admitted to a regional hospital with fever, recurrent bloody sputum, weight loss and left sided chest pain, and developed respiratory failure requiring ventilation. Investigations are summarised and reviewed and the diagnosis was eventually reached by TTE, CT and MRI scans, confirmed by coronary angiography. Successful emergency surgery to resect the aneurysm and put a vein graft to the LAD is described. The presentation and management of coronary giant aneurysm is reviewed

Imaging butyrylcholinesterase activity in Alzheimer's disease

No abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55890/1/21023_ftp.pd

Characterization of porous membranes using artificial neural networks

Porous membranes have been utilized intensively in a wide range of fields due to their special characteristics and a rigorous characterization of their microstructures is crucial for understanding their properties and improving the performance for target applications. A promising method for the quantitative analysis of porous structures leverages the physics-based generation of porous structures at the pore scale, which can be validated against real experimental microstructures, followed by building the process–structure–property relationships with data-driven algorithms such as artificial neural networks. In this study, a Variational AutoEncoder (VAE) neural network model is used to characterize the 3D structural information of porous materials and to represent them with low-dimensional latent variables, which further model the structure–property relationship and solve the inverse problem of process–structure linkage combined with the Bayesian optimization method. Our methods provide a quantitative way to learn structural descriptors in an unsupervised manner which can characterize porous microstructures robustly

Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra

Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-learning-based analysis approach shows good performance in identifying characteristic ions of the analyzed compounds that fit well with their chemical nature. Moreover, satisfying accuracy in identifying the compositions of unseen new samples is achieved. In addition, the scope and limitations of such a strategy in practical applications are discussed. This work presents a robust analytical method that can assist researchers in simplifying the analysis of the studied lithium compound samples, offering the potential for broader applications in other material systems

[ 11 C]NNC 12-0722 or [ 18 F]GBR 13119: Just what is “better”?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46836/1/259_2004_Article_BF00285593.pd

O4‐03‐06: The role of APOE genotype in early mild cognitive impairment (E‐MCI): Preliminary results from ADNI‐2

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152615/1/alzjjalz2012051651.pd

P2–200: Self‐ versus informant‐based cognitive complaints: Relation of E‐Cog scores to imaging, biomarkers and clinical Status in ADNI‐2

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152591/1/alzjjalz201305845.pd