ISA API: An open platform for interoperable life science experimental metadata

Background The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open source community specifications and software tools for enabling discovery, exchange, and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab—a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, the JSON serialization ISA-JSON was developed. Results In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters, and its growing user community. Conclusions The ISA API provides users with rich programmatic metadata-handling functionality to support automation, a common interface, and an interoperable medium between the 2 ISA formats, as well as with other life science data formats required for depositing data in public databases.

Gesture-Based Smart Switch

The project aims at making old/dum electric appliances smart for controlling remotely for easy and touchless/contactless operations via software operations. As in this era of covid-19, a button is the most common interface to interact with the digital world. It could be as simple as a light/fan switch. So our Smart Switch box can be used to replace the existing switches in home which produces sparks and results in fire accidents in a few situations. Considering the advantages of Wi-Fi, an advanced automation system was developed to control the appliances in the house.

Cargo competition for a dimerization interface restricts and stabilizes a bacterial protease adaptor

Bacterial protein degradation is a regulated process aided by protease adaptors that alter specificity of energy-dependent proteases. In Caulobacter crescentus, cell cycle–dependent protein degradation depends on a hierarchy of adaptors, such as the dimeric RcdA adaptor, which binds multiple cargo and delivers substrates to the ClpXP protease. RcdA itself is degraded in the absence of cargo, and how RcdA recognizes its targets is unknown. Here, we show that RcdA dimerization and cargo binding compete for a common interface. Cargo binding separates RcdA dimers, and a monomeric variant of RcdA fails to be degraded, suggesting that RcdA degradation is a result of self-delivery. Based on HDX-MS studies showing that different cargo rely on different regions of the dimerization interface, we generate RcdA variants that are selective for specific cargo and show cellular defects consistent with changes in selectivity. Finally, we show that masking of cargo binding by dimerization also limits substrate delivery to restrain overly prolific degradation. Using the same interface for dimerization and cargo binding offers an ability to limit excess protease adaptors by self-degradation while providing a capacity for binding a range of substrates.

Labeling and Probing the Silica Surface Using Mechanochemistry and 17O NMR Spectroscopy

In recent years, there has been increasing interest in developing cost-efficient, fast, and user-friendly ¹⁷O enrichment protocols to help understand the structure and reactivity of materials using ¹⁷O NMR. Here, we show for the first time how ball milling (BM) can be used to selectively and efficiently enrich the surface of fumed silica, which is widely used at the industrial scale. Short milling times (up to 15 min) allowed modulation of the enrichment level (up to ca. 5%) without significantly changing the nature of the material. High-precision ¹⁷O-compositions were measured at different milling times using LG-SIMS. High-resolution ¹⁷O NMR analyses (including at 35.2 T) allowed clear identification of the signals from siloxane (Si-O-Si) and silanols (Si-OH), while DNP analyses, performed using direct ¹⁷O polarization and indirect ¹⁷O{¹H} CP excitation, agreed with selectivelabeling of the surface. Information on the distribution of Si-OH environments at the surface was obtained from 2D ¹H-¹⁷O D-HMQC correlations. Finally, the surface-labeled silica was reacted with titania and using ¹⁷O DNP, their common interface was probed and Si-O-Ti bonds identified.

Labeling and Probing the Silica Surface Using Mechanochemistry and 17O NMR Spectroscopy

In recent years, there has been increasing interest in developing cost-efficient, fast, and user-friendly ¹⁷O enrichment protocols to help understand the structure and reactivity of materials using ¹⁷O NMR. Here, we show for the first time how ball milling (BM) can be used to selectively and efficiently enrich the surface of fumed silica, which is widely used at the industrial scale. Short milling times (up to 15 min) allowed modulation of the enrichment level (up to ca. 5%) without significantly changing the nature of the material. High-precision ¹⁷O-compositions were measured at different milling times using LG-SIMS. High-resolution ¹⁷O NMR analyses (including at 35.2 T) allowed clear identification of the signals from siloxane (Si-O-Si) and silanols (Si-OH), while DNP analyses, performed using direct ¹⁷O polarization and indirect ¹⁷O{¹H} CP excitation, agreed with selectivelabeling of the surface. Information on the distribution of Si-OH environments at the surface was obtained from 2D ¹H-¹⁷O D-HMQC correlations. Finally, the surface-labeled silica was reacted with titania and using ¹⁷O DNP, their common interface was probed and Si-O-Ti bonds identified.

A community effort for automatic detection of postictal generalized EEG suppression in epilepsy

Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentimes, researchers with a strong data science background do not understand the clinical challenges, while on the other hand, physicians do not know the capacity and limitation of state-of-the-art machine learning methods. The difficulty boils down to the lack of a common interface between two highly intelligent communities due to the privacy concerns and the disciplinary gap. The School of Biomedical Informatics (SBMI) at UTHealth is a pilot in connecting both worlds to promote interdisciplinary research. Recently, the Center for Secure Artificial Intelligence For hEalthcare (SAFE) at SBMI is organizing a series of machine learning healthcare hackathons for real-world clinical challenges. We hosted our first Hackathon themed centered around Sudden Unexpected Death in Epilepsy and finding ways to recognize the warning signs. This community effort demonstrated that interdisciplinary discussion and productive competition has significantly increased the accuracy of warning sign detection compared to the previous work, and ultimately showing a potential of this hackathon as a platform to connect the two communities of data science and medicine.

ISA API: An open platform for interoperable life science experimental metadata

BackgroundThe Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open-source community specifications and software tools for enabling discovery, exchange and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab – a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, a JSON serialization ISA-JSON was developed.ResultsIn this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python class objects. We describe the ISA API feature set, early adopters and its growing user community.ConclusionsThe ISA API provides users with rich programmatic metadata handling functionality to support automation, a common interface and an interoperable medium between the two ISA formats, as well as with other life science data formats required for depositing data in public databases.