How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science.

Exploratory synthesis has been the main generator of new inorganic materials for decades. However, our Edisonian and bias-prone processes of synthetic exploration alone are no longer sufficient in an age that demands rapid advances in materials development. In this work, we demonstrate an end-to-end attempt towards systematic, computer-aided discovery and laboratory synthesis of inorganic crystalline compounds as a modern alternative to purely exploratory synthesis. Our approach initializes materials discovery campaigns by autonomously mapping the synthetic feasibility of a chemical system using density functional theory with AI feedback. Following expert-driven down-selection of newly generated phases, we use solid-state synthesis and in situ characterization via hot-stage X-ray diffraction in order to realize new ternary oxide phases experimentally. We applied this strategy in six ternary transition-metal oxide chemistries previously considered well-explored, one of which culminated in the discovery of two novel phases of calcium ruthenates. Detailed characterization using room temperature X-ray powder diffraction, 4D-STEM and SQUID measurements identifies the structure and composition and confirms distinct properties, including distinct defect concentrations, of one of the new phases formed in our experimental campaigns. While the discovery of a new material guided by AI and DFT theory represents a milestone, our procedure and results also highlight a number of critical gaps in the process that can inform future efforts towards the improvement of AI-coupled methodologies.

Molecular mechanism of miRNA-23a in sepsis-induced lung injury.

OBJECTIVE: MicroRNA-23a-3p (miR-23a) is a novel gene regulator involved in inflammation. This study aimed to explore the molecular mechanism of miR-23a in sepsis-induced lung injury both in vitro and in vivo. METHODS: Lipopolysaccharide (LPS)- and ATP-stimulated human myeloid leukemia mononuclear cells (THP-1) and Human Bronchial Epithelial Cells (BEAS-2B) cell lines were used, while cecal ligation and puncture (CLP)-induced sepsis BABL/c mice were constructed. The mRNA expression levels of interleukin (IL)-18, IL-1ß, and miR-23a were determined, and Western blotting was used to measure CXCR4/PTEN/PI3K/AKT signaling. The concentrations of cytokines and Nod-like receptor family pyrin domain-containing 3 (NLRP3) were determined using an enzyme-linked immunosorbent assay. Lung tissue of mice was subjected to hematoxylin-eosin staining for examining myocardial injury. RESULTS: MiR-23a inhibited NLRP3 inflammasome activation in LPS- and ATP-stimulated THP-1 and BEAS-2B cells (P<0.05). Overexpression of miR-23a decreased the lactate dehydrogenase release rate in the cells (P<0.05). Meanwhile, miR-23a overexpression decreased the concentration and gene expression of IL-1ß and IL-18 in CXCR4 positive cells (P<0.05). Conversely, miR-23a knockdown increased the concentration and gene expression of IL-1ß and IL-18 (P<0.05). Additionally, PTEN and p53 proteins were up-regulated in miR-23a mimic group and down-regulated in miR-23a inhibitor group (P<0.05). Furthermore, miR-23a expression was decreased in sepsis-induced lung injury mice (P<0.05). MiR-23a overexpression reduced the sepsis-induced lung injury probably by inhibiting acetylcholinesterase activity and expression levels of IL-1ß, IL-18, capase-1, and NLRP3 (P<0.05). CONCLUSION: miR-23a can significantly alleviate sepsis-induced lung injury in CLP-induced septic mice and LPS-stimulated cell lines by suppressing NLRP3 inflammasome activation and inflammatory response, while promoting the CXCR4/PTEN/PI3K/AKT pathway.

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.

Mechanism of miR-338-3p in sepsis-induced acute lung injury via indirectly modulating ATF4.

Sepsis is recognized as an inflammation-related syndrome in response to invading pathogens. Many patients suffer from sepsis including transplant recipients. Lipopolysaccharide (LPS) is known to trigger sepsis-related organ dysfunction. This study expounded on the possible effect of microRNA (miR)-338-3p in sepsis-induced acute lung injury (ALI). Firstly, human bronchial epithelial cell line 16HBE received LPS treatment to establish the cell models of sepsis-induced ALI. The expression patterns of miR-338-3p, long non-coding RNA OPA-interacting protein 5 antisense transcript 1 (lncRNA OIP5-AS1), and activating transcription factor 4 (ATF4) in 16HBE cells were examined. Afterwards, 16HBE cell viability, the apoptosis rate, and the levels of inflammation and lactate dehydrogenase (LDH) were determined to assess the degree of cell injury. We disclosed that LPS treatment triggered 16HBE cell injury, downregulated miR-338-3p, and upregulated OIP5-AS1 and ATF4. miR-338-3p overexpression repressed LPS-induced 16HBE cell injury. miR-338-3p diminished OIP5-AS1 stability via binding to OIP5-AS1 and downregulated OIP5-AS1 expression and OIP5-AS1 can enhance ATF4 mRNA stability and upregulate ATF4 mRNA level. The rescue experiments showed that ATF4 overexpression aggravated LPS-induced 16HBE cell injury. Overall, miR-338-3p overexpression decreased OIP5-AS1 expression and stability and further downregulated ATF4 mRNA level, thereby mitigating LPS-induced 16HBE cell injury.

Variable Temperature Behaviour of the Hybrid Double Perovskite MA2KBiCl6.

Perovskite-related materials show very promising properties in many fields. Pb-free perovskites are particularly interesting, because of the toxicity of Pb. In this study, hybrid double perovskite MA2KBiCl6 (MA = methylammonium cation) was found to have interesting variable temperature behaviours. Both variable temperature single crystal X-ray diffraction, synchrotron powder diffraction, and Raman spectroscopy were conducted to reveal a rhombohedral to cubic phase transition at around 330 K and an order to disorder transition for inorganic cage below 210 K.

Machine Learning Guided Dopant Selection for Metal Oxide-Based Photoelectrochemical Water Splitting: The Case Study of Fe2 O3 and CuO.

Doping is an effective strategy for tuning metal oxide-based semiconductors for solar-driven photoelectrochemical (PEC) water splitting. Despite decades of extensive research effort, the dopant selection is still largely dependent on a trial-and-error approach. Machine learning (ML) is promising in providing predictable insights on the dopant selection for high-performing PEC systems because it can uncover correlations from the seemingly ambiguous linkages between vast features of dopants and the PEC performance of doped photoelectrodes. Herein, the authors successfully build ML model to predict the doping effect of 17 metal dopants into hematite (Fe2 O3 ), a prototype photoelectrode material. Their findings disclose the critical parameters from the 10 intrinsic features of each dopant. The model is further experimentally validated by the coherent prediction on Y and La dopants' behaviors. Further interpretation of the ML model suggests that the chemical state is the most significant selection criteria, meanwhile, dopants with higher metal-oxygen bond formation enthalpy and larger ionic radius are favored in improving the charge separation and transfer (CST) in the Fe2 O3 photoanodes. The generic feature of this ML guided selection criteria has been further extended to CuO-based photoelectrodes showing improved CST by alkaline metal ions doping.

Predicting Antimicrobial Activity of Conjugated Oligoelectrolyte Molecules via Machine Learning.

New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug takes decades. Although progress in molecular property prediction using machine-learning methods has opened up new pathways for aiding the antibiotics development process, many existing solutions rely on large data sets and finding structural similarities to existing antibiotics. Challenges remain in modeling unconventional antibiotic classes that are drawing increasing research attention. In response, we developed an antimicrobial activity prediction model for conjugated oligoelectrolyte molecules, a new class of antibiotics that lacks extensive prior structure-activity relationship studies. Our approach enables us to predict the minimum inhibitory concentration for E. coli K12, with 21 molecular descriptors selected by recursive elimination from a set of 5305 descriptors. This predictive model achieves an R2 of 0.65 with no prior knowledge of the underlying mechanism. We find the molecular representation optimum for the domain is the key to good predictions of antimicrobial activity. In the case of conjugated oligoelectrolytes, a representation reflecting the three-dimensional shape of the molecules is most critical. Although it is demonstrated with a specific example of conjugated oligoelectrolytes, our proposed approach for creating the predictive model can be readily adapted to other novel antibiotic candidate domains.

circ­Grm1 promotes pulmonary artery smooth muscle cell proliferation and migration via suppression of GRM1 expression by FUS.

Pulmonary arterial hypertension is a progressive and fatal disease. Recent studies suggest that circular RNA (circRNAs/circs) can regulate various biological processes, including cell proliferation. Therefore, it is possible that circRNA may have important roles in pulmonary artery smooth muscle cell proliferation in hypoxic pulmonary hypertension (HPH). The aim of the present study was to determine the role and mechanism of circRNA­glutamate metabotropic receptor 1 (circ­Grm1; mmu_circ_0001907) in pulmonary artery smooth muscle cell (PASMC) proliferation and migration in HPH. High­throughput transcriptome sequencing was used to screen circRNAs and targeted genes involved in HPH. Cell Counting Kit­8 (CCK­8), 5­ethynyl­2­deoxyuridine and wound healing assays were employed to assess cell viability and migration. Reverse transcription­quantitative PCR and western blotting were used to detect target gene expression in different groups. Bioinformatical approaches were used to predict the interaction probabilities of circ­Grm1 and Grm1 with FUS RNA binding protein (FUS). The interactions of circ­Grm1, Grm1 and FUS were evaluated using RNA silencing and RNA immunoprecipitation assays. The results demonstrated that circ­Grm1 was upregulated in hypoxic PASMCs. Further experiments revealed that the knockdown of circ­Grm1 could suppress the proliferation and migration of hypoxic PASMCs. Transcriptome sequencing revealed that Grm1 could be the target gene of circ­Grm1. It was found that circ­Grm1 could competitively bind to FUS and consequently downregulate Grm1. Moreover, Grm1 could inhibit the function of circ­Grm1 by promoting the proliferative and migratory abilities of hypoxic PASMCs. The results also demonstrated that circ­Grm1 influenced the biological functions of PASMCs via the Rap1/ERK pathway by regulating Grm1. Overall, the current results suggested that circ­Grm1 was associated with HPH and promoted the proliferation and migration of PASMCs via suppression of Grm1 expression through FUS.

Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning.

Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (<100°C). We close by demonstrating the methylammonium-containing perovskite solar cells showing negligible efficiency loss compared to its initial efficiency after 1800 hours of working under illumination at 30°C.

Safety and Transcriptome Analysis of Live Attenuated Brucella Vaccine Strain S2 on Non-pregnant Cynomolgus Monkeys Without Abortive Effect on Pregnant Cynomolgus Monkeys.

Brucellosis, caused by Brucella spp., is an important zoonotic disease leading to enormous economic losses in livestock, posing a great threat to public health worldwide. The live attenuated Brucella suis (B. suis) strain S2, a safe and effective vaccine, is widely used in animals in China. However, S2 vaccination in animals may raise debates and concerns in terms of safety to primates, particularly humans. In this study, we used cynomolgus monkey as an animal model to evaluate the safety of the S2 vaccine strain on primates. In addition, we performed transcriptome analysis to determine gene expression profiling on cynomolgus monkeys immunized with the S2 vaccine. Our results suggested that the S2 vaccine was safe for cynomolgus monkeys. The transcriptome analysis identified 663 differentially expressed genes (DEGs), of which 348 were significantly upregulated and 315 were remarkably downregulated. The Gene Ontology (GO) classification and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these DEGs were involved in various biological processes (BPs), including the chemokine signaling pathway, actin cytoskeleton regulation, the defense response, immune system processing, and the type-I interferon signaling pathway. The molecular functions of the DEGs were mainly comprised of 2'-5'-oligoadenylate synthetase activity, double-stranded RNA binding, and actin-binding. Moreover, the cellular components of these DEGs included integrin complex, myosin II complex, and blood microparticle. Our findings alleviate the concerns over the safety of the S2 vaccine on primates and provide a genetic basis for the response from a mammalian host following vaccination with the S2 vaccine.