Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance.

Direct Conversion of Perovskite Thin Films into Nanowires with Kinetic Control for Flexible Optoelectronic Devices.

With significant progress in the past decade, semiconductor nanowires have demonstrated unique features compared to their thin film counterparts, such as enhanced light absorption, mechanical integrity and reduced therma conductivity, etc. However, technologies of semiconductor thin film still serve as foundations of several major industries, such as electronics, displays, energy, etc. A direct path to convert thin film to nanowires can build a bridge between these two and therefore facilitate the large-scale applications of nanowires. Here, we demonstrate that methylammonium lead iodide (CH3NH3PbI3) nanowires can be synthesized directly from perovskite film by a scalable conversion process. In addition, with fine kinetic control, morphologies, and diameters of these nanowires can be well-controlled. Based on these perovskite nanowires with excellent optical trapping and mechanical properties, flexible photodetectors with good sensitivity are demonstrated.

Causal structure discovery identifies risk factors and early brain markers related to evolution of white matter hyperintensities.

Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30-90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.

A novel method for causal structure discovery from EHR data and its application to type-2 diabetes mellitus.

Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.

Challenges and Opportunities with Causal Discovery Algorithms: Application to Alzheimer's Pathophysiology.

Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships. We used Alzheimer's disease (AD), a complex progressive disease, as a model because the well-established evidence provides a "gold-standard" causal graph for evaluation. We evaluated two CSD methods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to discover this structure from data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used structural equation models (which is not designed for CSD) as control. We applied these methods under three scenarios defined by increasing amounts of background knowledge provided to the methods. The methods were evaluated by comparing the resulting causal relationships with the "gold standard" graph that was constructed from literature. Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard. For best results, CSD algorithms should be used with longitudinal data providing as much prior knowledge as possible.

Perovskite Photovoltachromic Supercapacitor with All-Transparent Electrodes.

Photovoltachromic cells (PVCCs) are of great interest for the self-powered smart windows of architectures and vehicles, which require widely tunable transmittance and automatic color change under photostimuli. Organolead halide perovskite possesses high light absorption coefficient and enables thin and semitransparent photovoltaic device. In this work, we demonstrate co-anode and co-cathode photovoltachromic supercapacitors (PVCSs) by vertically integrating a perovskite solar cell (PSC) with MoO3/Au/MoO3 transparent electrode and electrochromic supercapacitor. The PVCSs provide a seamless integration of energy harvesting/storage device, automatic and wide color tunability, and enhanced photostability of PSCs. Compared with conventional PVCC, the counter electrodes of our PVCSs provide sufficient balancing charge, eliminate the necessity of reverse bias voltage for bleaching the device, and realize reasonable in situ energy storage. The color states of PVCSs not only indicate the amount of energy stored and energy consumed in real time, but also enhance the photostability of photovoltaic component by preventing its long-time photoexposure under fully charged state of PVCSs. This work designs PVCS devices for multifunctional smart window applications commonly made of glass.