Cut-and-Paste DNA Insertion with Engineered Type V-K CRISPR-associated Transposases

CRISPR-associated transposases (CASTs) enable recombination-independent, multi-kilobase DNA insertions at RNA-programmed genomic locations. Type V-K CASTs offer distinct technological advantages over type I CASTs given their smaller coding size, fewer components, and unidirectional insertions. However, the utility of type V-K CASTs is hindered by a replicative transposition mechanism that results in a mixture of desired simple cargo insertions and undesired plasmid co-integrate products. Here, we overcome this limitation by engineering new CASTs with dramatically improved product purity. To do so, we compensate for the absence of the TnsA subunit in multiple type V-K CASTs by engineering a Homing Endonuclease-assisted Large-sequence Integrating CAST compleX, or HELIX system. HELIX utilizes a nicking homing endonuclease (nHE) fused to TnsB to restore the 5-prime nicking capability needed for dual-nicking of the DNA donor. By leveraging distinct features of both type V-K and type I systems, HELIX enables cut-and-paste DNA insertion with up to 99.3% simple insertion product purity, while retaining robust integration efficiencies on genomic targets. Furthermore, we demonstrate the versatility of this approach by generating HELIX systems for other CAST orthologs. We also establish the feasibility of creating a minimal, 3-component HELIX, simplifying the number of proteins that must be expressed. Together, HELIX streamlines and improves the application of CRISPR-based transposition technologies, eliminating barriers for efficient and specific RNA-guided DNA insertions.

Legal and Regulatory Framework for AI Solutions in Healthcare in EU, US, China, and Russia: New Scenarios after a Pandemic

The COVID-19 crisis has exposed some of the most pressing challenges affecting healthcare and highlighted the benefits that robust integration of digital and AI technologies in the healthcare setting may bring. Although medical solutions based on AI are growing rapidly, regulatory issues and policy initiatives including ownership and control of data, data sharing, privacy protection, telemedicine, and accountability need to be carefully and continually addressed as AI research requires robust and ethical guidelines, demanding an update of the legal and regulatory framework all over the world. Several recently proposed regulatory frameworks provide a solid foundation but do not address a number of issues that may prevent algorithms from being fully trusted. A global effort is needed for an open, mature conversation about the best possible way to guard against and mitigate possible harms to realize the potential of AI across health systems in a respectful and ethical way. This conversation must include national and international policymakers, physicians, digital health and machine learning leaders from industry and academia. If this is done properly and in a timely fashion, the potential of AI in healthcare will be realized.

Identifying At-Risk Communities and Key Vulnerability Indicators in the COVID-19 Pandemic

Throughout the COVID-19 pandemic, certain communities have been disproportionately exposed to detrimental health outcomes and socioeconomic injuries. Quantifying community needs is crucial for identifying testing and service deserts, effectively allocating resources, and informing funding and decision making. We have constructed research-driven metrics measuring the public health and economic impacts of COVID-19 on vulnerable populations. In this work we further examine and validate these indices by training supervised models to predict proxy outcomes and analyzing the feature importances to identify gaps in our original metric design. The indices analyzed in this work are unique among COVID-19 risk assessments due to their robust integration of disparate data sources. Together, they enable more effective responses to COVID-19 driven health inequities.

Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review

Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user’s situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications.

MONTE enables serial immunopeptidome, ubiquitylome, proteome, phosphoproteome, acetylome analyses of sample-limited tissues

Multiomic characterization of patient tissues provides insights into the function of different biological pathways in the context of disease. Much work has been done to serialize proteome and post-translational modification (PTM) analyses to conserve precious patient samples. However, characterizing clinically relevant tissues with multi-ome workflows that have distinct sample processing requirements remains challenging. To overcome the obstacles of combining enrichment workflows that have unique input amounts and utilize both label free and chemical labeling strategies, we developed a highly-sensitive multi-omic networked tissue enrichment (MONTE) workflow for the full analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome and acetylome all from the same tissue sample. The MONTE workflow enables identification of a median of 9,000 HLA-I peptides, 6,000 HLA-II peptides, 10,000 Ub sites, 12,000 proteins, 20,000 phosphorylation sites and 15,000 acetylation sites from patient LUAD tumors. Because all omes are generated from the exact same tissue sample, there is less biological variability in the data enabling more robust integration. The information available in MONTE datasets facilitates the identification of putative immunotherapeutic targets, such as CT antigens and neoantigens presented by HLA complexes, as well as reveal insights into how disease-specific changes in protein expression, protein degradation, cell signaling, metabolic, and epigenetic pathways are involved in disease pathology and treatment.

Membrane Integration into PDMS-free Microfluidic Platforms for Organ-on-Chip and Analytical Chemistry Applications

Membranes play a crucial role in many microfluidic systems, enabling versatile applications in highly diverse research fields. However, the tight and robust integration of membranes into microfluidic systems requires complex...