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In well-established first-order conditioning experiments, the concurrence of a sensory cue with reinforcement forms an association, allowing the cue to predict future reinforcement. In the insect mushroom body, a brain region central to learning and memory, such associations are encoded in the synapses between its intrinsic and output neurons. This process is mediated by the activity of dopaminergic neurons that encode reinforcement signals. In second-order conditioning, a new sensory cue is paired with an already established one that presumably activates dopaminergic neurons due to its predictive power of the reinforcement. We explored minimal circuit motifs in the mushroom body for their ability to support second-order conditioning using mechanistic models. We found that dopaminergic neurons can either be activated directly by the mushroom body’s intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrated that the circuit motifs differ in their computational efficiency and robustness. Beyond previous research, we suggest an additional motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters. 

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As 3+ and 6.8 pM for Cr 6+ , 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy. 

ABSTRACT Inactivation of DNA mismatch repair propels colorectal cancer (CRC) tumorigenesis. CRCs exhibiting e levated m icrosatellite a lterations at s elected t etranucleotide repeats (EMAST) show reduced nuclear MutS homolog 3 (MSH3) expression with surrounding inflammation and portend poor patient outcomes. MSH3 reversibly exits from the nucleus to the cytosol in response to the proinflammatory cytokine interleukin-6 (IL-6), suggesting that MSH3 may be a shuttling protein. In this study, we manipulated three putative nuclear localization (NLS1 to -3) and two potential nuclear export signals (NES1 and -2) within MSH3. We found that both NLS1 and NLS2 possess nuclear import function, with NLS1 responsible for nuclear localization within full-length MSH3. We also found that NES1 and NES2 work synergistically to maximize nuclear export, with both being required for IL-6-induced MSH3 export. We examined a 27-bp deletion (Δ27bp) within the polymorphic exon 1 that occurs frequently in human CRC cells and neighbors NLS1. With oxidative stress, MSH3 with this deletion (Δ27bp MSH3) localizes to the cytoplasm, suggesting that NLS1 function in Δ27bp MSH3 is compromised. Overall, MSH3’s shuttling in response to inflammation enables accumulation in the cytoplasm; reduced nuclear MSH3 increases EMAST and DNA damage. We suggest that polymorphic sequences adjacent to NLS1 may enhance cytosolic retention, which has clinical implications for inflammation-associated neoplastic processes. 

In the 21st Century, research is increasingly data- and computation-driven. Researchers, funders, and the larger community today emphasize the traits of openness and reproducibility. In March 2017, 13 mostly early-career research leaders who are building their careers around these traits came together with ten university leaders (presidents, vice presidents, and vice provosts), representatives from four funding agencies, and eleven organizers and other stakeholders in an NIH- and NSF-funded one-day, invitation-only workshop titled "Imagining Tomorrow's University." Workshop attendees were charged with launching a new dialog around open research – the current status, opportunities for advancement, and challenges that limit sharing. The workshop examined how the internet-enabled research world has changed, and how universities need to change to adapt commensurately, aiming to understand how universities can and should make themselves competitive and attract the best students, staff, and faculty in this new world. During the workshop, the participants re-imagined scholarship, education, and institutions for an open, networked era, to uncover new opportunities for universities to create value and serve society. They expressed the results of these deliberations as a set of 22 principles of tomorrow's university across six areas: credit and attribution, communities, outreach and engagement, education, preservation and reproducibility, and technologies. Activities that follow on from workshop results take one of three forms. First, since the workshop, a number of workshop authors have further developed and published their white papers to make their reflections and recommendations more concrete. These authors are also conducting efforts to implement these ideas, and to make changes in the university system.  Second, we plan to organise a follow-up workshop that focuses on how these principles could be implemented. Third, we believe that the outcomes of this workshop support and are connected with recent theoretical work on the position and future of open knowledge institutions.