Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks

In the present paper, we introduce a new neural network-based tool for the prediction of formation energies of atomic structures based on elemental and structural features of Voronoi-tessellated materials. We provide a concise overview of the connection between the machine learning and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, and how new data can be incorporated into the model to tune it to a specific material system. The present work resulted in three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve a mean average error (MAE) of 28, 40, and 42 meV/atom, respectively. The second model provides better predictions on materials far from ones reported in OQMD, while the third reduces the computational cost by a factor of 8. We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a GUI interface. By virtue of this, it can be included in DFT calculations routines at nearly no cost

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development

Molecular basis of USP7 inhibition by selective small-molecule inhibitors

Ubiquitination controls the stability of most cellular proteins, and its deregulation contributes to human diseases including cancer. Deubiquitinases remove ubiquitin from proteins, and their inhibition can induce the degradation of selected proteins, potentially including otherwise 'undruggable' targets. For example, the inhibition of ubiquitin-specific protease 7 (USP7) results in the degradation of the oncogenic E3 ligase MDM2, and leads to re-activation of the tumour suppressor p53 in various cancers. Here we report that two compounds, FT671 and FT827, inhibit USP7 with high affinity and specificity in vitro and within human cells. Co-crystal structures reveal that both compounds target a dynamic pocket near the catalytic centre of the auto-inhibited apo form of USP7, which differs from other USP deubiquitinases. Consistent with USP7 target engagement in cells, FT671 destabilizes USP7 substrates including MDM2, increases levels of p53, and results in the transcription of p53 target genes, induction of the tumour suppressor p21, and inhibition of tumour growth in mice

Promoting children's positive intergroup attitudes towards stigmatized groups: Extended contact and multiple classification skills training

Two studies were conducted to evaluate interventions, based upon the extended contact hypothesis and multiple classification skills training, which aimed to promote children's positive intergroup attitudes towards two stigmatized groups. Study I tested whether extended contact and multiple classification skills training changed out-group attitudes towards the disabled among 6-9 year-old children. Out-group attitudes were significantly more positive only in the extended contact condition compared to the control. Study 2 involved four conditions: control, extended contact, modified multiple classification skills training and a combination of both interventions. Again, only the 6-11 year-old children who experienced the extended contact interventions (extended contact and combined) showed significantly more positive attitudes towards the refugee out-group compared to the control. The implications of these findings for the development of prejudice-reduction strategies in children will be discussed

Generative deep learning as a tool for inverse design of high entropy refractory alloys

Generative deep learning is powering a wave of new innovations in materials design. This article discusses the basic operating principles of these methods and their advantages over rational design through the lens of a case study on refractory high-entropy alloys for ultra-high-temperature applications. We present our computational infrastructure and workflow for the inverse design of new alloys powered by these methods. Our preliminary results show that generative models can learn complex relationships to generate novelty on demand, making them a valuable tool for materials informatics

Measurement of Microcystin Activity in Human Plasma Using Immunocapture and Protein Phosphatase Inhibition Assay

Microcystins are toxic chemicals generated by certain freshwater cyanobacteria. These chemicals can accumulate to dangerous levels during harmful algal blooms. When exposed to microcystins, humans are at risk of hepatic injury, including liver failure. Here, we describe a method to detect microcystins in human plasma by using immunocapture followed by a protein phosphatase inhibition assay. At least 279 microcystins have been identified, and most of these compounds share a common amino acid, the Adda side chain. We targeted this Adda side chain using a commercial antibody and extracted microcystins from human samples for screening and analysis. To quantitate the extracted microcystins, we fortified plasma with microcystin-LR, one of the most well-studied, commonly detected, and toxic microcystin congeners. The quantitation range for the detection of microcystin in human plasma using this method is 0.030–0.50 ng/mL microcystin-LR equivalents. This method detects unconjugated and conjugated forms (cysteine and glutathione) of microcystins. Quality control sample accuracies varied between 98.9% and 114%, with a precision of 7.18–15.8%. Finally, we evaluated plasma samples from a community health surveillance project of Florida residents living or working near harmful algae blooms

Data standards for plant phenotyping: MIAPPE and its implementations

International audiencePlant Phenotyping data management following the FAIR (Findable, Accessible, Interoperable, Resusable) is highly challenging because of its heterogenity. Thus, simply integrating and consolidating data within a single dataset like a phenotyping network is already a complicated task which is even more complex when trying to link different datasets together. To adress this problem, the Minimal Information About Plant Phenotyping Experiment standard construction has been initiated four years ago, with the help of experts from European infrastructures and institutes like Elixir, Emphasis, INRA, WUR, iBet, IPK, EBI and IPG PAS. It adresses the need of data publication and reuse through a checklist that formalize and document the minimal metadata necessary to ensure long term FAIRness of field or greenhouse datasets, including high througputs phenotyping ones. This list has been implemented in several databases like GnpIS or eDale, in a file format, ISA Tab, in a web service, the Breeding API and an RDF implementation is under construction. We will review those implementations, show its current adoption state and detail the plans for the future evolutions of the standard