Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models

(Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of neural networks remains: their numerous parameters are challenging to interpret and explain. Thus, neural networks are often labeled as black boxes, and their results often elude human interpretation. The new and active field of physics-informed neural networks attempts to mitigate this disadvantage by designing deep neural networks on the basis of mechanical knowledge. By using this a priori knowledge, deeper and more complex neural networks became feasible, since the mechanical assumptions can be explained. However, the internal reasoning and explanation of neural network parameters remain mysterious. Complementary to the physics-informed approach, we propose a first step towards a physics-explaining approach, which interprets neural networks trained on mechanical data a posteriori. This proof-of-concept explainable artificial intelligence approach aims at elucidating the black box of neural networks and their high-dimensional representations. Therein, the principal component analysis decorrelates the distributed representations in cell states of RNNs and allows the comparison to known and fundamental functions. The novel approach is supported by a systematic hyperparameter search strategy that identifies the best neural network architectures and training parameters. The findings of three case studies on fundamental constitutive models (hyperelasticity, elastoplasticity, and viscoelasticity) imply that the proposed strategy can help identify numerical and analytical closed-form solutions to characterize new materials

Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate

Lithium-ion batteries with solid electrolytes offer safety, higher energy density and higher long-term performance, which are promising alternatives to conventional liquid electrolyte batteries. Lithium aluminum titanium phosphate (LATP) is one potential solid electrolyte candidate due to its high Li-ion conductivity. To evaluate its performance, influences of the experimental factors on the materials design need to be investigated systematically. In this work, a materials design strategy based on machine learning (ML) is employed to design experimental conditions for the synthesis of LATP. In the variation of parameters, we focus on the tolerance against the possible deviations in the concentration of the precursors, as well as the influence of sintering temperature and holding time. Specifically, models built with different design selection strategies are compared based on the training data assembled from previous laboratory experiments. The best one is then chosen to design new experiment parameters, followed by measuring the corresponding properties of the newly synthesized samples. A previously unknown sample with ionic conductivity of 1.09 × 10$^{-3}$ S cm$^{-1}$ is discovered within several iterations. In order to further understand the mechanisms governing the high ionic conductivity of these samples, the resulting phase compositions and crystal structures are studied with X-ray diffraction, while the microstructures of sintered pellets are investigated by scanning electron microscopy. Our studies demonstrate the advantages of applying machine learning in designing experimental conditions by the synthesis of desired materials, which can effectively help researchers to reduce the number of required experiments

Esterase-Responsive Polyglycerol-Based Nanogels for Intracellular Drug Delivery in Rare Gastrointestinal Stromal Tumors

Rare gastrointestinal stromal tumors (GISTs) are caused by mutations in the KIT and PDGFRA genes. Avapritinib (BLU-285) is a targeted selective inhibitor for mutated KIT and PDGFRA receptors that can be used to treat these tumors. However, there are subtypes of GISTs that exhibit resistance against BLU-285 and thus require other treatment strategies. This can be addressed by employing a drug delivery system that transports a combination of drugs with distinct cell targets. In this work, we present the synthesis of esterase-responsive polyglycerol-based nanogels (NGs) to overcome drug resistance in rare GISTs. Using inverse nanoprecipitation mediated with inverse electron-demand Diels–Alder cyclizations (iEDDA) between dPG-methyl tetrazine and dPG-norbornene, multi-drug-loaded NGs were formed based on a surfactant-free encapsulation protocol. The obtained NGs displayed great stability in the presence of fetal bovine serum (FBS) and did not trigger hemolysis in red blood cells over a period of 24 h. Exposing the NGs to Candida Antarctica Lipase B (CALB) led to the degradation of the NG network, indicating the capability of targeted drug release. The bioactivity of the loaded NGs was tested in vitro on various cell lines of the GIST-T1 family, which exhibit different drug resistances. Cell internalization with comparable uptake kinetics of the NGs could be confirmed by confocal laser scanning microscopy (CLSM) and flow cytometry for all cell lines. Cell viability and live cell imaging studies revealed that the loaded NGs are capable of intracellular drug release by showing similar IC50 values to those of the free drugs. Furthermore, multi-drug-loaded NGs were capable of overcoming BLU-285 resistance in T1-α-D842V + G680R cells, demonstrating the utility of this carrier system

In Vivo Imaging of Vesicular Monoamine Transporters in Human Brain Using [ 11 C]Tetrabenazine and Positron Emission Tomography

The pharmacokinetics of [ 11 CJtetrabenazine, a high-affinity radioligand for the monoamine vesicular transporter, were determined in living human brain using in vivo imaging by positron emission tomography (PET). The radiotracer showed high brain uptake and rapid washout from all brain regions with relatively slower clearance from regions of highest concentrations of monoamine vesicular transporters (striatum), resulting in clear differential visualization of these structures at short intervals after injection (10–20 min). As the first human PET imaging study of a vesicular neurotransmitter transporter, these experiments demonstrate that external imaging of vesicular transporters forms a new and valuable approach to the in vivo quantification of monoaminergic neurons, with potential application to the in vivo study of neurodegenerative disorders such as Parkinson's disease.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65743/1/j.1471-4159.1993.tb03521.x.pd

Comparison of Pittsburgh compound B and florbetapir in cross-sectional and longitudinal studies.

IntroductionQuantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B-based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design.MethodsPittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally.ResultsGlobal amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers.DiscussionAlthough the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers

Enzyme replacement reverses abnormal cerebrovascular responses in Fabry disease

BACKGROUND: Fabry disease is a lysosomal X-linked enzyme deficiency of α-galactosidase A associated with an increased mortality and morbidity due to renal failure, cardiac disease and early onset stroke. METHODS: We examined the functional blood flow response of the brain after visual stimulation (reversing checkerboard pattern), and cerebral vasoreactivity following acetazolamide (15 mg/kg) with [(15)O]H(2)O and positron emission tomography (PET) in Fabry disease. Twenty-six hemizygous patients (age range 19–47 years) were enrolled in a randomized double-blind placebo-controlled 6-month trial of enzyme replacement therapy administered by intravenous infusion every two weeks. Regional cerebral blood flow (rCBF) was measured with PET at the beginning and end of the trial. RESULTS: Fabry patients had a significantly greater increase in rCBF following visual stimulation and acetazolamide challenge compared to controls. Visual reactivity was normal. The time for recovery of the cerebral vasculature following acetazolamide was prolonged in Fabry patients compared to controls. The abnormal rCBF response induced by visual stimulation and acetazolamide decreased significantly following enzyme replacement therapy, as did the prolonged recovery of the cerebral vasculature. CONCLUSIONS: Enzyme replacement therapy reverses the exaggerated cerebrovascular response in Fabry disease

Comparing amyloid-β plaque burden with antemortem PiB PET in autosomal dominant and late-onset Alzheimer disease

Pittsburgh compound B (PiB) radiotracer for positron emission tomography (PET) imaging can bind to different types of amyloid-β plaques and blood vessels (cerebral amyloid angiopathy). However, the relative contributions of different plaque subtypes (diffuse versus cored/compact) to in vivo PiB PET signal on a region-by-region basis is incompletely understood. Of particular interest is whether the same staging schemes for summarizing amyloid-β burden are appropriate for both late-onset and autosomal dominant forms of Alzheimer disease (LOAD and ADAD). Here we compared antemortem PiB PET with follow-up postmortem estimation of amyloid-β burden using stereologic methods to estimate the relative area fraction of diffuse and cored/compact amyloid-β plaques across 16 brain regions in 15 individuals with ADAD and 14 individuals with LOAD. In ADAD, we found that PiB PET correlated with diffuse plaques in the frontal, parietal, temporal, and striatal regions commonly used to summarize amyloid-β burden in PiB PET, and correlated with both diffuse and cored/compact plaques in the occipital lobe and parahippocampal gyrus. In LOAD, we found that PiB PET correlated with both diffuse and cored/compact plaques in the anterior cingulate, frontal lobe (middle frontal gyrus), and parietal lobe, and showed additional correlations with diffuse plaque in the amygdala and occipital lobe, and with cored/compact plaque in the temporal lobe. Thus, commonly used PiB PET summary regions predominantly reflect diffuse plaque burden in ADAD and a mixture of diffuse and cored/compact plaque burden in LOAD. In direct comparisons of ADAD and LOAD, postmortem stereology identified much greater mean amyloid-β plaque burdens in ADAD versus LOAD across almost all brain regions studied. However, standard PiB PET did not recapitulate these stereologic findings, likely due to non-trivial amyloid-β plaque burdens in ADAD within the cerebellum and brainstem – commonly used reference regions in PiB PET. Our findings suggest that PiB PET summary regions correlate with amyloid-β plaque burden in both ADAD and LOAD; however, they might not be reliable in direct comparisons of regional amyloid-β plaque burden between the two forms of AD

Serum neurofilament light chain levels are associated with white matter integrity in autosomal dominant Alzheimer's disease

Neurofilament light chain (NfL) is a protein that is selectively expressed in neurons. Increased levels of NfL measured in either cerebrospinal fluid or blood is thought to be a biomarker of neuronal damage in neurodegenerative diseases. However, there have been limited investigations relating NfL to the concurrent measures of white matter (WM) decline that it should reflect. White matter damage is a common feature of Alzheimer's disease. We hypothesized that serum levels of NfL would associate with WM lesion volume and diffusion tensor imaging (DTI) metrics cross-sectionally in 117 autosomal dominant mutation carriers (MC) compared to 84 non-carrier (NC) familial controls as well as in a subset (N = 41) of MC with longitudinal NfL and MRI data. In MC, elevated cross-sectional NfL was positively associated with WM hyperintensity lesion volume, mean diffusivity, radial diffusivity, and axial diffusivity and negatively with fractional anisotropy. Greater change in NfL levels in MC was associated with larger changes in fractional anisotropy, mean diffusivity, and radial diffusivity, all indicative of reduced WM integrity. There were no relationships with NfL in NC. Our results demonstrate that blood-based NfL levels reflect WM integrity and supports the view that blood levels of NfL are predictive of WM damage in the brain. This is a critical result in improving the interpretability of NfL as a marker of brain integrity, and for validating this emerging biomarker for future use in clinical and research settings across multiple neurodegenerative diseases