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Background: DJ-1 is a protein whose mutation causes rare heritable forms of Parkinson’s disease (PD) and is of interest as a target for treating PD and other disorders. This work used high performance affinity microcolumns to screen and examine the binding of small molecules to DJ-1, as could be used to develop new therapeutics or to study the role of DJ-1 in PD. Non-covalent entrapment was used to place microgram quantities of DJ-1 in an unmodified form within microcolumns, which were then used in multiple studies to analyze binding by model compounds and possible drug candidates to DJ-1. Results: Several factors were examined in optimizing the entrapment method, including the addition of a reducing agent to maintain a reduced active site cysteine residue in DJ-1, the concentration of DJ-1 employed, and the entrapment times. Isatin was used as a known binding agent (dissociation constant, ~2.0 µM) and probe for DJ-1 activity. This compound gave good retention on 2.0 cm × 2.1 mm inner diameter DJ-1 microcolumns made under the final entrapment conditions, with a typical retention factor of 14 and elution in ~8 min at 0.50 mL/min. These DJ-1 microcolumns were used to evaluate the binding of small molecules that were selected in silico to bind or not to bind DJ-1. A compound predicted to have good binding with DJ-1 gave a retention factor of 122, an elution time of ~15 min at 0.50 mL/min, and an estimated dissociation constant for this protein of 0.5 µM. Significance: These chromatographic tools can be used in future work to screen additional possible binding agents for DJ-1 or adapted for examining drug candidates for other proteins. This work represents the first time protein entrapment has been deployed with DJ-1, and it is the first experimental confirmation of binding to DJ-1 by a small lead compound selected in silico. 

We are experiencing a revolution in vehicle operation, with fully automated robotaxis deployed and available for public use in major U.S. markets in 2023. These vehicles, while imperfect, already are arguably safer than the average human driver. Despite this rapid progress, there remain significant research and development problems that must be addressed; beyond this, there is an underdeveloped workforce for skilled researchers, developers, and practitioners in these areas, a fact that may delay necessary advances. We have created and run for two years a National Science Foundation funded Research Experience for Undergraduates (NSF REU) focused on solving both unmet research needs, and workforce development and pipeline programs. In our REU, which makes use of simulation and two full-scale, street-legal drive-by-wire electric vehicles with perception, planning, and control capabilities, our primary goals include to (1) provide hands-on experiences to undergraduate students who otherwise might not have research opportunities to learn fundamental theories in autonomous vehicle development, (2) allow students to design algorithms to practice software development and evaluation using real vehicles on real test courses, (3) strengthen their confidence, self-guided capabilities, and research skills, and (4) increase the number of students, including those from diverse backgrounds and technical disciplines, interested in graduate programs to ultimately provide a quality research and development workforce to both academia and industry. Over the initial two years, a cohort of 8 diverse students each year learned fundamental self-driving and computer networking skills including coding for drive-by-wire vehicles, computer vision, use of localization, and interpretation of richer sensor data, as well as network and communication protocols. The students were introduced to research ideation and publishing concepts, mentored in designing and testing hypotheses, and then involved in two challenges related to self-driving and networked vehicles. Two teams of 4 designed, implemented, tested various self-drive and V2X algorithms using real vehicles on a test course, analyzed/evaluated test results, wrote technical reports, and delivered presentations. After the summer program was over, the technical reports were published in peer reviewed conferences and journals. Survey results show that students attained significant & real-world computer science skills in autonomous vehicle development leveraging real vehicles available. The programs also increased research career interests and strengthened students’ confidence, self-guided capabilities, and research skills, while additionally supporting the development of workshop materials, simulators, and related content that provide valuable resources for others planning to develop an undergraduate curriculum to teach self-drive and networked vehicle development. 

Abstract Plants release back to the atmosphere about half of the CO 2 they capture by photosynthesis. Decreasing the rate of crop respiration could therefore potentially increase yields, store more carbon in the soil and draw down atmospheric CO 2 . However, decreasing respiration rate has had very little research effort compared to increasing photosynthesis, the historically dominant metabolic paradigm for crop improvement. Conceptual and technical advances, particularly in protein turnover and directed enzyme evolution, have now opened ways to trim the large fraction of respiration that fuels proteome maintenance by lowering the breakdown and resynthesis rates of enzymes and other proteins. In addition to being theoretically possible and practicable, exploring the reduction of respiration is prudential, given that it (i) has barely yet been tried and (ii) could help meet the challenges of sustaining crop productivity and managing atmospheric carbon.