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150 words) 13 Therapeutic antibody optimization is time and resource intensive, largely because it requires 14 low-throughput screening (10 3 variants) of full-length IgG in mammalian cells, typically resulting 15 in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-16 specificity from a massively diverse sequence space to identify globally optimized antibody 17 variants. Using a mammalian display platform and the therapeutic antibody trastuzumab, 18 rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-19 mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small 20 libraries (10 4 ) produced high quality data capable of training deep neural networks that 21 accurately predict antigen-binding based on antibody sequence. Deep learning is then used to 22 predict millions of antigen binders from an in silico library of ~10 8 variants, where experimental 23 testing of 30 randomly selected variants showed all 30 retained antigen specificity. The full set 24 of in silico predicted binders is then subjected to multiple developability filters, resulting in 25 thousands of highly-optimized lead candidates. With its scalability and capacity to interrogate 26 high-dimensional protein sequence space, deep learning offers great potential for antibody 27 engineering and optimization. 28 29 31 hybridomas, phage or yeast display libraries typically result in a number of potential lead candidates. 32However, the time and costs associated with lead candidate optimization often take up the majority of 33 the preclinical discovery and development cycle 1 . This is largely due to the fact that lead optimization 34 of antibody molecules consists of addressing multiple parameters in parallel, including expression level, 35 viscosity, pharmacokinetics, solubility, and immunogenicity 2,3 . Once a lead candidate is discovered, 36 additional engineering is often required; phage and yeast display offer a powerful method for high-37 throughput screening of large mutagenesis libraries (>10 9 ), however they are primarily only used for 38 increasing affinity or specificity to the target antigen 4 . The fact that nearly all therapeutic antibodies 39 require expression in mammalian cells as full-length IgG means that the remaining development and 40 optimization steps must occur in this context. Since mammalian cells lack the capability to stably 41 Deep learning enables therapeutic antibody optimization in mammalian cells 1 replicate plasmids, this last stage of development is done at very low-throughput, as elaborate cloning, 42 transfection and purification strategies must be implemented to screen libraries in the max range of 10 3 , 43 meaning only minor changes (e.g., point mutations) are screened 5 . Interrogating such a small fraction 44 of protein sequence space also implies that addressing one development issue will frequently cause 45 rise of another or even diminish antigen binding altogether, making multi-parameter optimization ve...

We autonomously directed a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or “drone” to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, were varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.

Characterization of COVID-19 antibodies has largely focused on memory B cells, however it is the antibody-secreting plasma cells that are directly responsible for the production of serum antibodies, which play a critical role in resolving SARS-CoV-2 infection. However, little is known about the specificity of plasma cells, largely because plasma cells lack surface antibody expression, thereby complicating their screening. Here, we describe a technology pipeline that integrates single-cell antibody repertoire sequencing and mammalian display to interrogate the specificity of plasma cells from 16 convalescent patients. Single-cell sequencing allows us to profile antibody repertoire features and identify expanded clonal lineages. Mammalian display screening is employed to reveal that 43 antibodies (of 132 candidates) derived from expanded plasma cell lineages are specific to SARS-CoV-2 antigens, including antibodies with high affinity to the SARS-CoV-2 receptor binding domain (RBD) that exhibit potent neutralization and broad binding to the RBD of SARS-CoV-2 variants (of concern/interest).

COVID-19 disease outcome is highly dependent on adaptive immunity from T and B lymphocytes, which play a critical role in the control, clearance and long-term protection against SARS-CoV-2. To date, there is limited knowledge on the composition of the T and B cell immune receptor repertoires [T cell receptors (TCRs) and B cell receptors (BCRs)] and transcriptomes in convalescent COVID-19 patients of different age groups. Here, we utilize single-cell sequencing (scSeq) of lymphocyte immune repertoires and transcriptomes to quantitatively profile the adaptive immune response in COVID-19 patients of varying age. We discovered highly expanded T and B cells in multiple patients, with the most expanded clonotypes coming from the effector CD8+ T cell population. Highly expanded CD8+ and CD4+ T cell clones show elevated markers of cytotoxicity (CD8: PRF1, GZMH, GNLY; CD4: GZMA), whereas clonally expanded B cells show markers of transition into the plasma cell state and activation across patients. By comparing young and old convalescent COVID-19 patients (mean ages = 31 and 66.8 years, respectively), we found that clonally expanded B cells in young patients were predominantly of the IgA isotype and their BCRs had incurred higher levels of somatic hypermutation than elderly patients. In conclusion, our scSeq analysis defines the adaptive immune repertoire and transcriptome in convalescent COVID-19 patients and shows important age-related differences implicated in immunity against SARS-CoV-2.