Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership.

Energy absorbing efficiency is a key determinant of a structure's ability to provide mechanical protection and is defined by the amount of energy that can be absorbed prior to stresses increasing to a level that damages the system to be protected. Here, we explore the energy absorbing efficiency of additively manufactured polymer structures by using a self-driving lab (SDL) to perform >25,000 physical experiments on generalized cylindrical shells. We use a human-SDL collaborative approach where experiments are selected from over trillions of candidates in an 11-dimensional parameter space using Bayesian optimization and then automatically performed while the human team monitors progress to periodically modify aspects of the system. The result of this human-SDL campaign is the discovery of a structure with a 75.2% energy absorbing efficiency and a library of experimental data that reveals transferable principles for designing tough structures.

Rational Structural Design of Polymer Pens for Energy-Efficient Photoactuation.

Photoactuated pens have emerged as promising tools for expedient, mask-free, and versatile nanomanufacturing. However, the challenge of effectively controlling individual pens in large arrays for high-throughput patterning has been a significant hurdle. In this study, we introduce novel generations of photoactuated pens and explore the impact of pen architecture on photoactuation efficiency and crosstalk through simulations and experiments. By introducing a thermal insulating layer and incorporating an air ap in the architecture design, we have achieved the separation of pens into independent units. This new design allowed for improved control over the actuation behavior of individual pens, markedly reducing the influence of neighboring pens. The results of our research suggest novel applications of photoactive composite films as advanced actuators across diverse fields, including lithography, adaptive optics, and soft robotics.

Bayesian reconstruction of magnetic resonance images using Gaussian processes.

A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic resonance (MR) images in k-space and use these libraries to identify optimal subsampling paths and reconstruction processes. Specifically, we compute a multivariate normal distribution based upon Gaussian processes using a publicly available library of T1-weighted images of healthy brains. We combine this library with physics-informed envelope functions to only retain meaningful correlations in k-space. This covariance function is then used to select a series of ring-shaped subsampling paths using Bayesian optimization such that they optimally explore space while remaining practically realizable in commercial MRI systems. Combining optimized subsampling paths found for a range of images, we compute a generalized sampling path that, when used for novel images, produces superlative structural similarity and error in comparison to previously reported reconstruction processes (i.e. 96.3% structural similarity and < 0.003 normalized mean squared error from sampling only 12.5% of the k-space data). Finally, we use this reconstruction process on pathological data without retraining to show that reconstructed images are clinically useful for stroke identification. Since the model trained on images of healthy brains could be directly used for predictions in pathological brains without retraining, it shows the inherent transferability of this approach and opens doors to its widespread use.

Characterization of complex structural variation in the CYP2D6-CYP2D7-CYP2D8 gene loci using single-molecule long-read sequencing.

Complex regions in the human genome such as repeat motifs, pseudogenes and structural (SVs) and copy number variations (CNVs) present ongoing challenges to accurate genetic analysis, particularly for short-read Next-Generation-Sequencing (NGS) technologies. One such region is the highly polymorphic CYP2D loci, containing CYP2D6, a clinically relevant pharmacogene contributing to the metabolism of >20% of common drugs, and two highly similar pseudogenes, CYP2D7 and CYP2D8. Multiple complex SVs, including CYP2D6/CYP2D7-derived hybrid genes are known to occur in different configurations and frequencies across populations and are difficult to detect and characterize accurately. This can lead to incorrect enzyme activity assignment and impact drug dosing recommendations, often disproportionally affecting underrepresented populations. To improve CYP2D6 genotyping accuracy, we developed a PCR-free CRISPR-Cas9 based enrichment method for targeted long-read sequencing that fully characterizes the entire CYP2D6-CYP2D7-CYP2D8 loci. Clinically relevant sample types, including blood, saliva, and liver tissue were sequenced, generating high coverage sets of continuous single molecule reads spanning the entire targeted region of up to 52 kb, regardless of SV present (n = 9). This allowed for fully phased dissection of the entire loci structure, including breakpoints, to accurately resolve complex CYP2D6 diplotypes with a single assay. Additionally, we identified three novel CYP2D6 suballeles, and fully characterized 17 CYP2D7 and 18 CYP2D8 unique haplotypes. This method for CYP2D6 genotyping has the potential to significantly improve accurate clinical phenotyping to inform drug therapy and can be adapted to overcome testing limitations of other clinically challenging genomic regions.

Screening for electrically conductive defects in thin functional films using electrochemiluminescence.

Multifunctional thin films in energy-related devices often must be electrically insulating where a single nanoscale defect can result in complete device-scale failure. Locating and characterizing such defects presents a fundamental problem where high-resolution imaging methods are needed to find defects, but imaging with high spatial resolution limits the field of view and thus the measurement throughput. Here, we present a novel high-throughput method for detecting sub-micron defects in insulating thin films by leveraging the electrochemiluminescence (ECL) of luminol. Through a systematic study of reagent concentrations, buffers, voltage, and excitation time, we identify optimized conditions under which it is possible to detect sub-micron defects at high-throughput. Extrapolating from the signal to background observed for detecting 440 nm wide lines and 620 nm diameter circles, we estimate the minimum detectable features to be lines as narrow as 2.5 nm in width and pinholes as small as 70 nm in radius. We further explore this method by using it to characterize a nominally insulating poly(phenylene oxide) film and find conductive defects that are cross-correlated with high-resolution atomic force microscopy to provide feedback to synthesis. Given this assay's inherent parallelizability and scalability, it is expected to have a major impact on the automated discovery of multifunctional films.

Quantitative nanopatterning of fg-scale liquids with dip-pen nanolithography.

The ability to precisely pattern nanoscale amounts of liquids is essential for biotechnology and high-throughput chemistry, but controlling fluid flow on these scales is very challenging. Scanning probe lithography methods such as dip-pen nanolithography (DPN) provide a mechanism to write fluids at the nanoscale, but this is an open loop process as methods to provide feedback while patterning sub-pg features have yet to be reported. Here, we demonstrate a novel method for programmably nanopatterning liquid features at the fg-scale through a combination of ultrafast atomic force microscopy probes, the use of spherical tips, and inertial mass sensing. We begin by investigating the required probe properties that would provide sufficient mass responsivity to detect fg-scale mass changes and find ultrafast probes to be capable of this resolution. Further, we attach a spherical bead to the tip of an ultrafast probe as we hypothesize that the spherical tip could hold a drop at its apex which both facilitates interpretation of inertial sensing and maintains a consistent fluid environment for reliable patterning. We experimentally find that sphere-tipped ultrafast probes are capable of reliably patterning hundreds of features in a single experiment. Analyzing the changes in the vibrational resonance frequency during the patterning process, we find that drift in the resonance frequency complicates analysis, but that it can be removed through a systematic correction. Subsequently, we quantitatively study patterning using sphere-tipped ultrafast probes as a function of retraction speed and dwell time to find that the mass of fluid transferred can be modulated by greater than an order of magnitude and that liquid features as small as 6 fg can be patterned and resolved. Taken together, this work addresses a persistent concern in DPN by enabling quantitative feedback for nanopatterning of aL-scale features and lays the foundation for programmably nanopatterning fluids.

A framework for materials informatics education through workshops.

Abstract: The burgeoning field of materials informatics necessitates a focus on educating the next generation of materials scientists in the concepts of data science, artificial intelligence (AI), and machine learning (ML). In addition to incorporating these topics in undergraduate and graduate curricula, regular hands-on workshops present the most effective medium to initiate researchers to informatics and have them start applying the best AI/ML tools to their own research. With the help of the Materials Research Society (MRS), members of the MRS AI Staging Committee, and a dedicated team of instructors, we successfully conducted workshops covering the essential concepts of AI/ML as applied to materials data, at both the Spring and Fall Meetings in 2022, with plans to make this a regular feature in future meetings. In this article, we discuss the importance of materials informatics education via the lens of these workshops, including details such as learning and implementing specific algorithms, the crucial nuts and bolts of ML, and using competitions to increase interest and participation.

A CRISPR-enhanced metagenomic NGS test to improve pandemic preparedness.

The lack of preparedness for detecting and responding to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogen (i.e., COVID-19) has caused enormous harm to public health and the economy. Testing strategies deployed on a population scale at day zero, i.e., the time of the first reported case, would be of significant value. Next-generation sequencing (NGS) has such capabilities; however, it has limited detection sensitivity for low-copy-number pathogens. Here, we leverage the CRISPR-Cas9 system to effectively remove abundant sequences not contributing to pathogen detection and show that NGS detection sensitivity of SARS-CoV-2 approaches that of RT-qPCR. The resulting sequence data can also be used for variant strain typing, co-infection detection, and individual human host response assessment, all in a single molecular and analysis workflow. This NGS work flow is pathogen agnostic and, therefore, has the potential to transform how large-scale pandemic response and focused clinical infectious disease testing are pursued in the future.

Direct pumping of polar fluids with traveling-wave dielectrophoresis.

Efficiently pumping fluids without moving parts in extremely miniaturized formats is challenging. Here, we propose and numerically explore a new type of fluid pump in which a series of electrodes driven at different phases produce a force directly on the molecules of the fluid. This effect is based on traveling-wave dielectrophoresis (twDEP), which has been observed to drive the motion of colloidal particles. Here, we leverage the time needed for fluid molecules with permanent dipoles to align with the applied field to maintain a phase lag between the applied field and the molecular polarization. While requiring operation in the GHz range, this effect is predicted to be efficient due to its ability to directly drive bulk fluid motion. We begin by establishing the foundational equations for this effect and performing finite element simulations to determine its magnitude in a model geometry. By combining theory and a systematic series of calculations, we validate that twDEP pumps should exhibit a fluid flow that scales as the voltage squared divided by the electrode period and that it should increase with the complex permittivity of the fluid and decrease with increasing viscosity. This results in a general equation that predicts the performance of twDEP pumps. Collectively, these computations provide a blueprint for producing twDEP pumps of polar fluids such as water and ethanol. We conclude by noting that the growing interest in high power microwave technology along with metasurfaces to locally tailor phase could provide a path to realizing twDEP pumps in practice.

Experimental observation of metal-organic framework-polymer interaction forces and intercalation.

We attach a MOF crystallite to an atomic force microscope cantilever to realize a system for rapidly and quantitatively studying the interaction between single-crystal MOFs and polymer films. Using this method, we find evidence of polymer intercalation into MOF pores. This approach can accelerate composite design.

Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering.

This article reviews recent developments in the applications of machine learning, data-driven modeling, transfer learning, and autonomous experimentation for the discovery, design, and optimization of soft and biological materials. The design and engineering of molecules and molecular systems have long been a preoccupation of chemical and biomolecular engineers using a variety of computational and experimental techniques. Increasingly, researchers have looked to emerging and established tools in artificial intelligence and machine learning to integrate with established approaches in chemical science to realize powerful, efficient, and in some cases autonomous platforms for molecular discovery, materials engineering, and process optimization. This review summarizes the basic principles underpinning these techniques and highlights recent successful example applications in autonomous materials discovery, transfer learning, and multi-fidelity active learning.

Electric field induced macroscopic cellular phase of nanoparticles.

A suspension of nanoparticles with very low volume fraction is found to assemble into a macroscopic cellular phase that is composed of particle-rich walls and particle-free voids under the collective influence of AC and DC voltages. Systematic study of this phase transition shows that it was the result of electrophoretic assembly into a two-dimensional configuration followed by spinodal decomposition into particle-rich walls and particle-poor cells mediated principally by electrohydrodynamic flow. This mechanistic understanding reveals two characteristics needed for a cellular phase to form, namely (1) a system that is considered two dimensional and (2) short-range attractive, long-range repulsive interparticle interactions. In addition to determining the mechanism underpinning the formation of the cellular phase, this work presents a method to reversibly assemble microscale continuous structures out of nanoscale particles in a manner that may enable the creation of materials that impact diverse fields including energy storage and filtration.

The epithelial splicing regulator ESRP2 is epigenetically repressed by DNA hypermethylation in Wilms tumour and acts as a tumour suppressor.

Wilms tumour (WT), an embryonal kidney cancer, has been extensively characterised for genetic and epigenetic alterations, but a proportion of WTs still lack identifiable abnormalities. To uncover DNA methylation changes critical for WT pathogenesis, we compared the epigenome of foetal kidney with two WT cell lines, filtering our results to remove common cancer-associated epigenetic changes and to enrich for genes involved in early kidney development. This identified four hypermethylated genes, of which ESRP2 (epithelial splicing regulatory protein 2) was the most promising for further study. ESRP2 was commonly repressed by DNA methylation in WT, and this occurred early in WT development (in nephrogenic rests). ESRP2 expression was reactivated by DNA methyltransferase inhibition in WT cell lines. When ESRP2 was overexpressed in WT cell lines, it inhibited cellular proliferation in vitro, and in vivo it suppressed tumour growth of orthotopic xenografts in nude mice. RNA-seq of the ESRP2-expressing WT cell lines identified several novel splicing targets. We propose a model in which epigenetic inactivation of ESRP2 disrupts the mesenchymal to epithelial transition in early kidney development to generate WT.

Using simulation to accelerate autonomous experimentation: A case study using mechanics.

Autonomous experimentation (AE) accelerates research by combining automation and machine learning to perform experiments intelligently and rapidly in a sequential fashion. While AE systems are most needed to study properties that cannot be predicted analytically or computationally, even imperfect predictions can in principle be useful. Here, we investigate whether imperfect data from simulation can accelerate AE using a case study on the mechanics of additively manufactured structures. Initially, we study resilience, a property that is well-predicted by finite element analysis (FEA), and find that FEA can be used to build a Bayesian prior and experimental data can be integrated using discrepancy modeling to reduce the number of needed experiments ten-fold. Next, we study toughness, a property not well-predicted by FEA and find that FEA can still improve learning by transforming experimental data and guiding experiment selection. These results highlight multiple ways that simulation can improve AE through transfer learning.

Closed-Loop Nanopatterning of Liquids with Dip-Pen Nanolithography.

The ability to reliably manipulate small quantities of liquids is the backbone of high-throughput chemistry, but the continual drive for miniaturization necessitates creativity in how nanoscale samples of liquids are handled. Here, we describe a closed-loop method for patterning liquid samples on pL to sub-fL scales using scanning probe lithography. Specifically, we employ tipless scanning probes and identify liquid properties that enable probe-sample transport that is readily tuned using probe withdrawal speed. Subsequently, we introduce a novel two-harmonic inertial sensing scheme for tracking the mass of liquid on the probe. Finally, this is combined with a fluid mechanics-based iterative control scheme that selects printing conditions to meet a target feature mass to enable closed-loop patterning with better than 1% accuracy and ∼4% precision in terms of mass. Taken together, these advances address a pervasive issue in scanning probe lithography, namely, real-time closed-loop control over patterning, and position scanning probe lithography of liquids as a candidate for the robust nanoscale manipulation of liquids for advanced high-throughput chemistry.

A Case of Wernicke Encephalopathy Secondary to Anorexia Nervosa Complicated by Refeeding Syndrome and Takotsubo Cardiomyopathy.

BACKGROUND Wernicke encephalopathy (WE) is a neurological condition commonly associated with sustained alcohol abuse. However, it should be noted that disorders resulting in severe malnutrition, such as anorexia nervosa (AN), can precipitate nonalcoholic WE. AN is a life threatening psychological and eating disorder defined by inappropriate weight loss from food restriction due to the fear of gaining weight and immoderate desire to be thin. Treatment of those suffering with AN can often be complicated by severe electrolyte derangements after caloric intake termed refeeding syndrome. Although extremely rare, severe cardiomyopathy and ultimately death may occur in patients from AN. CASE REPORT Herein describes the case of a 20-year-old female with AN induced WE complicated by refeeding syndrome and hemodynamic compromise in the setting of findings consistent with takotsubo cardiomyopathy. She required ventilatory and hemodynamic support with aggressive intravenous thiamine and phosphorus repletion. Nutritional supplementation was imperative and carefully administered throughout her hospitalization. Her symptoms improved over the course of a few weeks with an ultimate reversal of her cardiomyopathy. CONCLUSIONS Given the morbidity surrounding AN, practitioners should exhibit caution when caring for those with severe nutritional deficiencies. Clinicians must monitor for severe electrolyte abnormalities and offer aggressive repletion. In addition to electrolyte derangements, severe cardiomyopathy may result as a rare sequela of the aforementioned complications associated with AN. Moreover, it is imperative to understand that patients with AN have the highest mortality of any psychiatric disorder and early intervention is necessary for survival in this vulnerable patient population.

Reinforcing Magnetorheological Fluids with Highly Anisotropic 2D Materials.

The front cover artwork is provided by the group of Professor Keith Brown at Boston University. The image shows the magnetorheological fluid in a pressure-driven flow and highlights the length scales of the magnetic particles and highly anisotropic 2D sheets. Read the full text of the Article at 10.1002/cphc.202000948.

Patient perspectives and involvement in precision medicine research.

The Kidney Precision Medicine Project will advance understanding of chronic kidney disease attributed to diabetes or hypertension and acute kidney injury through a protocol kidney biopsy used for deep phenotyping with state-of-the-art methodology. To guide scientific inquiry toward clinically meaningful benefit, patients are equal partners for priority setting, study design and conduct, and dissemination of findings. Patients from stakeholder organizations, recruitment sites, tissue interrogation sites, and the Central Hub are represented on the Community Engagement Committee. This unique collaboration between patients and scientists has set a new standard for inclusion in precision medicine research.

Rationale and design of the Kidney Precision Medicine Project.

Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.