Statistical analysis of correlated expression data from high throughput experiments.

In the sessions of Materials Genome Initiative (MGI) and Materials Informatics (MI) at recent international and domestic conferences, majority of research presentations are on material prediction using a combination of computational chemistry and machine learning. On the other hand, the contents incorporating high-throughput synthesis and evaluation experiments are only occasionally seen. It is also true that the number of samples that can be handled per hour is small compared to the former data science.However, if a series of experimental processes linked to synthesis, evaluation, and analysis is made user-friendly and high-throughput, it is possible to efficiently generate multi-condition and multi-component data in a unified experimental environment such as starting materials. As a result, even when creating a data set by extracting text from a paper, the data shortage can be supplemented with experimental data.Unlike thin films and polymers, powder synthesis is a heterogeneous reaction that requires the use of a wet process to perform high-throughput experiments. We have hitherto developed powder synthesis apparatus equipped with a micropump for solution dispensing, and high-throughput exploration system for powder and thin-film library based on electrostatic spray deposition consisted by combining multiple syringe pumps and a high-voltage power supply. By using these system, we have been exploring multi-component cathode materials for lithium ion battery and thermoelectric materials. In addition, recently, high-throughput experiments in a high-pressure environment of 200 MPa and 500ºC are possible.If the time required for physical property evaluation and data analysis is the same as before, even if only the synthesis based on experiments becomes high throughput, the attractiveness of MI will be weakened. Therefore, it is necessary to continue to develop jigs and software for efficient evaluation and analysis of huge libraries obtained in high-throughput experiments.In this presentation, we will introduce the exploration for A-site and B-site substitutes of perovskite-type CaMnO3, which is expected as an n-type thermoelectric material. In the preliminary search, it was confirmed that Ca1-x Bi x Mn1-y Ni y O3 contributes to the improvement of thermoelectric power by substituting elements at each site. As the next step, the amount of Bi substitution was determined to be within 10% from the combination of high-throughput synthesis and high-throughput Seebeck coefficient measurement. Synchrotron XRD and XAFS measurements were used to investigate the correlation between physical properties and crystal structure. We have succeeded in collecting data without filling the capillaries and making pellets by developing special tools. Synchrotron XRD and XAFS measurements were used to investigate the correlation between physical properties and crystal structure. We have succeeded in collecting data without filling the capillaries and making pellets by developing special tools. We also developed software that automates Rietveld analysis, although we need basic knowledge to set up an initial structural model. As a result, we have made it easier to generate data sets for visualizing crystallographic data and physical property data at once. In the obtained Bi-substituted CaMnO3 powder, when Bi was 8% or less, the conductivity and power factor increased due to the increase in carrier concentration. At Bi8% and higher, PF reaches its limit value, which involves a large change in the MnO6 octahedral plane bond distances Mn-O1 (1) and Mn-O1 (2), that is, a significant increase in octahedral distortion. High-throughput experiments including the collection of crystallographic information through the development of these technologies are expected to be an effective tool for future machine learning.

The depletion of traditional fossil fuels and growing energy concerns have led to the development of efficient, carbon-neutral energy conversion technologies powered by renewable sources. Among these, green hydrogen production through electrochemical water splitting is widely considered a promising strategy for energy storage. The proton exchange membrane (PEM) system in various water electrolysis technologies is particularly noteworthy for its high current densities, excellent voltage efficiency, and minimal hydrogen cross-diffusion, bringing it closer to commercialization. However, the efficiency of water splitting is constrained by the slow oxygen evolution reactions (OER), the anodic reaction in the process. Artificial intelligence (AI) is revolutionizing numerous fields, including science and technology, by streamlining complex tasks. High-throughput experiments (HTE), aided by AI, provide an accelerated pathway for catalyst discovery, overcoming the challenges posed by traditional, time-consuming experimental methods. Metal oxide catalysts are attractive for OER due to their low-cost transition metals, high structural stability, and element substitutability. However, the reaction mechanisms of oxides, including reaction pathways, kinetic barriers, and surface reconstruction, highlight the need to explore their structure-performance relationships for deeper insights. The complex chemical space offers countless potential materials for synthesis, which cannot be addressed through traditional reliance on heuristics and extensive testing. Researchers are increasingly utilizing AI to predict material properties, enabling the design of new materials by navigating chemical space. Given the complexity of optimizing materials with multidimensional compositions (potentially exceeding > 200,000 candidates based on pyrochlores), novel experimental methods are essential for advancing effective OER catalysts. This work reveals the relationship among catalyst synthesis, reaction mechanisms, and performance (activity/stability) by establishing a combined AI-HTE platform, which includes a self-built database, a state-of-the-art AI model, and high-throughput (HT) experiments/ calculations. Meanwhile, for the first time, this work successfully implement AI to achieve green hydrogen production and drive renewable energy storage and transition.

Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering

In shotgun proteomics, high-throughput mass spectrometry experiments and the subsequent data analysis produce thousands to millions of hypothetical peptide identifications. The common way to estimate the false discovery rate (FDR) of peptide identifications is the target-decoy database search strategy, which is efficient and accurate for large datasets. However, the legitimacy of the target-decoy strategy for protein-modification-centric studies has rarely been rigorously validated. It is often the case that a global FDR is estimated for all peptide identifications including both modified and unmodified peptides, but that only a subgroup of identifications with a certain type of modification is focused on. As revealed recently, the subgroup FDR of modified peptide identifications can differ dramatically from the global FDR at the same score threshold, and thus the former, when it is of interest, should be separately estimated. However, rare modifications often result in a very small number of modified peptide identifications, which makes the direct separate FDR estimation inaccurate because of the inadequate sample size. This paper presents a method called the transferred FDR for accurately estimating the FDR of an arbitrary number of modified peptide identifications. Through flexible use of the empirical data from a target-decoy database search, a theoretical relationship between the subgroup FDR and the global FDR is made computable. Through this relationship, the subgroup FDR can be predicted from the global FDR, allowing one to avoid an inaccurate direct estimation from a limited amount of data. The effectiveness of the method is demonstrated with both simulated and real mass spectra.

ABSTRACTThe High Throughput Experimentation (HTE) project of the Joint Center for Artificial Photosynthesis (JCAP, http://solarfuelshub.org/) performs accelerated discovery of new earth-abundant photoabsorbers and electrocatalysts. Through collaboration within the DOE solar fuels hub and with the broader research community, the new materials will be utilized in devices that efficiently convert solar energy, water and carbon dioxide into transportation fuels. JCAP-HTE builds high-throughput pipelines for the synthesis, screening and characterization of photoelectrochemical materials. In addition to a summary of these pipelines, we will describe several new screening instruments for high throughput (photo-)electrochemical measurements. These instruments are not only optimized for screening against solar fuels requirements, but also provide new tools for the broader combinatorial materials science community. We will also describe the high throughput discovery, follow-on verification, and device implementation of a new quaternary metal oxide catalyst. This rapid technology development from discovery to device implementation is a hallmark of the multi-faceted JCAP research effort.

Opportunities for catalyst discovery and development: Integrating surface science and theory with high throughput methods

High Throughput Experimentation (HTE) has revolutionized the pharmaceutical industry over the last two decades, developing from a technology for screening compounds against biological targets, to now being used for everything from parallel synthesis of diverse libraries of analogues and optimization of catalytic systems and reaction conditions, to screening for optimal crystallization solvents and API polymorphs. Many pharmaceutical companies (and several academic institutions) have invested in HTE capabilities (often including automation) to support their drug discovery pipelines and fundamental research. With HTE workflows now being firmly established and many successful HTE groups to draw inspiration from, new facilities being set up today have options on where and how to build and integrate new high throughput and automation capabilities into their organizations. This chapter discusses and classifies different options for the build and integration of high throughput experimentation resources into existing chemistry departments.

Peeling off the layers: Skin taping and a novel proteomics approach to study atopic dermatitis

Genetic Control of Individual Differences in Gene-Specific Methylation in Human Brain

Development of the Movement Disorders Interpretation Bias Scale and psychometric evaluation in adults with Huntington's disease.

High-throughput experimentation is a fast and efficient methodology for materials discovery, screening, and optimization. It has, therefore, garnered a lot of attention from battery researchers as a potential way to accelerate the development of next-generation Li-ion batteries and battery systems beyond Li-ion. However, its successful use in battery materials discovery has been extremely limited due to a variety of challenges including: (i) difficulties in synthesizing mg-scale samples that are comparable to bulk samples made by industrial methods, (ii) limited high-throughput characterization acting like a bottleneck even after high throughput synthesis is developed, and (iii) high cost of commercially available high-throughput instrumentation. Here, we develop high throughput techniques for solid-state electrolytes.First, a novel synthesis approach is used to make 64 mg-scale solid electrolytes simultaneously. The powder samples are prepared by a citrate sol-gel method using an automated dispensing system. These powders were formulated into sixty-four pellets using a home-made high-throughput pellet press die. The samples are then sintered at high temperature to yield the solid electrolytes. The proof of concept here is performed on the Li-La-Ti-O pseudo-ternary system that includes the perovskites Li3xLa2/3-xTiO3, considered candidates for solid-state electrolytes for high energy lithium-ion batteries. Given that vacancies and other defects play an essential role in ionic conductivity, the screening of complete phase diagrams has the potential to reveal phases with optimized defect contents and thus yield improved solid electrolytes.We have therefore developed a high-throughput characterization suite for solid electrolytes. First, we use a high throughput X-ray diffractometer capable of producing Rietveld quality patterns for mg-scale samples in 10 min such that 64 patterns can be obtained in under 12 h. This system is readily used to produce structural phase diagrams in ternary or even quaternary systems. Next, a high throughput electrochemical impedance spectrometer is used to map the ionic conductivities onto the phase diagrams. Finally, high throughput electrochemistry is used to determine the stability window of the electrolytes. All aspects of this workflow are demonstrated on the Li-La-Ti-O system. This now permits the synthesis and full characterization of approximately 192 compositions per week with one researcher only. This approach is well suited to determine the structure-property relations in complex composition spaces.The complete ternary Li-La-Ti phase diagram synthesized at 1150 °C with slow cooling was determined and will be presented in detail. The total (ionic + electronic) conductivity of the phase diagram was mapped by the electrochemical impedance spectroscopy. Quenching was performed to verify that transformations during slow cooling are moderate in this system. Consequences for the development of other solid electrolytes will also be discussed.

The development of a scalable and highly reproducible in vitro tumor microenvironment (TME) platform still sheds light on new insights into cancer metastasis mechanisms and anticancer therapeutic strategies. Here, we present an all-in-one injection molded plastic array three-dimensional culture platform (All-in-One-IMPACT) that integrates vascularized tumor spheroids for highly reproducible, high-throughput experimentation. This device allows the formation of self-assembled cell spheroids on a chip by applying the hanging drop method to the cell culture channel. Then, when the hydrogel containing endothelial cells and fibroblasts is injected, the spheroid inside the droplet can be patterned together in three dimensions along the culture channel. In just two steps above, we can build a vascularized TME within a defined area. This process does not require specialized user skill and minimizes error-inducing steps, enabling both reproducibility and high throughput of the experiment. We have successfully demonstrated the process, from spheroid formation to tumor vascularization, using patient-derived cancer cells (PDCs) as well as various cancer cell lines. Furthermore, we performed combination therapies with Taxol (paclitaxel) and Avastin (bevacizumab), which are used in standard care for metastatic cancer. The All-in-One IMPACT is a powerful tool for establishing various anticancer treatment strategies through the development of a complex TME for use in high-throughput experiments.

To accelerate the discovery of new or improved homogeneous catalysts, research groups in industry and academia have embraced high throughput experimentation (HTE). Such methodologies consist of preparing and testing large numbers of catalysts in parallel. Homogeneous metal catalysts are very well-suited for HTE, since in many cases, they can be prepared by simply mixing a metal precursor and a ligand. However, an HTE program requires a large set of chemically diverse ligands, i.e. a ligand library. In this review, we describe five different approaches for assembling ligand libraries based on an extensive survey of the literature. These approaches are based on commercial ligands, modular ligands, mixtures of ligands, supramolecular ligands or ligands prepared via the tools of biochemistry.

Huntington disease (HD) is a neurodegenerative disorder caused by an expansion of polyglutamines in the first exon of huntingtin (HTT), which confers aggregation-promoting properties to amino-terminal fragments of the protein (N-HTT). Mutant N-HTT aggregates are enriched for ubiquitin and contain ubiquitin E3 ligases, thus suggesting a role for ubiquitination in aggregate formation. Here, we report that tumor necrosis factor receptor-associated factor 6 (TRAF6) binds to WT and polyQ-expanded N-HTT in vitro as well as to endogenous full-length proteins in mouse and human brain in vivo. Endogenous TRAF6 is recruited to cellular inclusions formed by mutant N-HTT. Transient overexpression of TRAF6 promotes WT and mutant N-HTT atypical ubiquitination with Lys6, Lys27, and Lys29 linkage formation. Both interaction and ubiquitination seem to be independent from polyQ length. In cultured cells, TRAF6 enhances mutant N-HTT aggregate formation, whereas it has no effect on WT N-HTT protein localization. Mutant N-HTT inclusions are enriched for ubiquitin staining only when TRAF6 and Lys6, Lys27, and Lys29 ubiquitin mutants are expressed. Finally, we show that TRAF6 is up-regulated in post-mortem brains from HD patients where it is found in the insoluble fraction. These results suggest that TRAF6 atypical ubiquitination warrants investigation in HD pathogenesis.

The development of an efficient photoanode remains the primary materials challenge in the establishment of a scalable technology for solar water splitting. The typical photoanode architecture consists of a semiconductor light absorber coated with a metal oxide that serves a combination of functions, including corrosion protection, electrocatalysis, light trapping, hole transport, and elimination of deleterious recombination sites. To provide a more efficient exploration of metal oxide coatings for a given light absorber, we introduce a high throughput methodology wherein a uniform BiVO4 library is coated with 858 unique metal oxides covering a range of metal oxide loadings and the full Ni-La-Co-Ce oxide or Ni-Fe-Co-Ce oxide psuedo-quaternary composition spaces. Photoelectrochemical characterization of each photoanode reveals that approximately one third of the coatings lower the photoanode performance while select combinations of metal oxide composition and loading provide up to a 14-fold increase in the maximum photoelectrochemical power generation for oxygen evolution in pH 13 electrolyte. Particular Ce-rich coatings also exhibit an anti-reflection effect that further amplifies the performance, yielding a 20-fold enhancement in power conversion efficiency compared to bare BiVO4. By use of in situ optical spectroscopy and comparisons between the metal oxide coatings and their extrinsic optical and electrocatalytic properties, we present a suite of data-driven discoveries, including composition regions which form optimal interfaces with BiVO4 and photoanodes that are suitable for integration with a photocathode due to their excellent power conversion and solar transmission efficiencies. The initial high throughput discoveries were extended and validated through follow-up high throughput investigations and conventional photoelectrochemical measurements. The high throughput experimentation and informatics provides a powerful platform for both identifying the pertinent interfaces for further study and discovering high performance photoanodes for incorporation into efficient water splitting devices.