Health systems are heavily promoting patient portals. However, limited health literacy (HL) can restrict online communication via secure messaging (SM) because patients’ literacy skills must be sufficient to convey and comprehend content while clinicians must encourage and elicit communication from patients and match patients’ literacy level. This paper describes the Employing Computational Linguistics to Improve Patient-Provider Secure Email (ECLIPPSE) study, an interdisciplinary effort bringing together scientists in communication, computational linguistics, and health services to employ computational linguistic methods to (1) create a novel Linguistic Complexity Profile (LCP) to characterize communications of patients and clinicians and demonstrate its validity and (2) examine whether providers accommodate communication needs of patients with limited HL by tailoring their SM responses. We will study >5 million SMs generated by >150,000 ethnically diverse type 2 diabetes patients and >9000 clinicians from two settings: an integrated delivery system and a public (safety net) system. Finally, we will then create an LCP-based automated aid that delivers real-time feedback to clinicians to reduce the linguistic complexity of their SMs. This research will support health systems’ journeys to become health literate healthcare organizations and reduce HL-related disparities in diabetes care.

We previously reported a recombinant protein production system based on a geminivirus replicon that yields high levels of vaccine antigens and monoclonal antibodies in plants. The bean yellow dwarf virus (BeYDV) replicon generates massive amounts of DNA copies, which engage the plant transcription machinery. However, we noticed a disparity between transcript level and protein production, suggesting that mRNAs could be more efficiently utilized. In this study, we systematically evaluated genetic elements from human, viral, and plant sources for their potential to improve the BeYDV system. The tobacco extensin terminator enhanced transcript accumulation and protein production compared to other commonly used terminators, indicating that efficient transcript processing plays an important role in recombinant protein production.

Evaluation of human-derived 5′ untranslated regions (UTRs) indicated that many provided high levels of protein production, supporting their cross-kingdom function. Among the viral 5′ UTRs tested, we found the greatest enhancement with the tobacco mosaic virus omega leader. An analysis of the 5′ UTRs from the Arabidopsis thaliana and Nicotinana benthamiana photosystem I K genes found that they were highly active when truncated to include only the near upstream region, providing a dramatic enhancement of transgene production that exceeded that of the tobacco mosaic virus omega leader. The tobacco Rb7 matrix attachment region inserted downstream from the gene of interest provided significant enhancement, which was correlated with a reduction in plant cell death. Evaluation of Agrobacterium strains found that EHA105 enhanced protein production and reduced cell death compared to LBA4301 and GV3101. We used these improvements to produce Norwalk virus capsid protein at >20% total soluble protein, corresponding to 1.8 mg/g leaf fresh weight, more than twice the highest level ever reported in a plant system. We also produced the monoclonal antibody rituximab at 1 mg/g leaf fresh weight.

Increasing temperatures have been shown to impact soil biogeochemical processes, although the corresponding changes to the underlying microbial functional communities are not well understood. Alterations in the nitrogen (N) cycling functional component are particularly important as N availability can affect microbial decomposition rates of soil organic matter and influence plant productivity. To assess changes in the microbial component responsible for these changes, the composition of the N-fixing (nifH), and denitrifying (nirS, nirK, nosZ) soil microbial communities was assessed by targeted pyrosequencing of functional genes involved in N cycling in two major biomes where the experimental effect of climate warming is under investigation, a tallgrass prairie in Oklahoma (OK) and the active layer above permafrost in Alaska (AK). Raw reads were processed for quality, translated with frameshift correction, and a total of 313,842 amino acid sequences were clustered and linked to a nearest neighbor using reference datasets. The number of OTUs recovered ranged from 231 (NifH) to 862 (NirK). The N functional microbial communities of the prairie, which had experienced a decade of experimental warming were the most affected with changes in the richness and/or overall structure of NifH, NirS, NirK and NosZ. In contrast, the AK permafrost communities, which had experienced only 1 year of warming, showed decreased richness and a structural change only with the nirK-harboring bacterial community. A highly divergent nirK-harboring bacterial community was identified in the permafrost soils, suggesting much novelty, while other N functional communities exhibited similar relatedness to the reference databases, regardless of site. Prairie and permafrost soils also harbored highly divergent communities due mostly to differing major populations.

MALDI-TOF MS has been utilized as a reliable and rapid tool for microbial fingerprinting at the genus and species levels. Recently, there has been keen interest in using MALDI-TOF MS beyond the genus and species levels to rapidly identify antibiotic resistant strains of bacteria. The purpose of this study was to enhance strain level resolution for Campylobacter jejuni through the optimization of spectrum processing parameters using a series of designed experiments. A collection of 172 strains of C. jejuni were collected from Luxembourg, New Zealand, North America, and South Africa, consisting of four groups of antibiotic resistant isolates. The groups included: (1) 65 strains resistant to cefoperazone (2) 26 resistant to cefoperazone and beta-lactams (3) 5 strains resistant to cefoperazone, beta-lactams, and tetracycline, and (4) 76 strains resistant to cefoperazone, teicoplanin, amphotericin, B and cephalothin.

Initially, a model set of 16 strains (three biological replicates and three technical replicates per isolate, yielding a total of 144 spectra) of C. jejuni was subjected to each designed experiment to enhance detection of antibiotic resistance. The most optimal parameters were applied to the larger collection of 172 isolates (two biological replicates and three technical replicates per isolate, yielding a total of 1,031 spectra). We observed an increase in antibiotic resistance detection whenever either a curve based similarity coefficient (Pearson or ranked Pearson) was applied rather than a peak based (Dice) and/or the optimized preprocessing parameters were applied. Increases in antimicrobial resistance detection were scored using the jackknife maximum similarity technique following cluster analysis. From the first four groups of antibiotic resistant isolates, the optimized preprocessing parameters increased detection respective to the aforementioned groups by: (1) 5% (2) 9% (3) 10%, and (4) 2%. An additional second categorization was created from the collection consisting of 31 strains resistant to beta-lactams and 141 strains sensitive to beta-lactams. Applying optimal preprocessing parameters, beta-lactam resistance detection was increased by 34%. These results suggest that spectrum processing parameters, which are rarely optimized or adjusted, affect the performance of MALDI-TOF MS-based detection of antibiotic resistance and can be fine-tuned to enhance screening performance.

Specification of PM_2.5 transmission characteristics is important for pollution control and policymaking. We apply higher-order organization of complex networks to identify major potential PM_2.5 contributors and PM_2.5 transport pathways of a network of 189 cities in China. The network we create in this paper consists of major cities in China and contains information on meteorological conditions of wind speed and wind direction, data on geographic distance, mountains, and PM_2.5 concentrations. We aim to reveal PM_2.5 mobility between cities in China. Two major conclusions are revealed through motif analysis of complex networks. First, major potential PM_2.5 pollution contributors are identified for each cluster by one motif, which reflects movements from source to target. Second, transport pathways of PM_2.5 are revealed by another motif, which reflects transmission routes. To our knowledge, this is the first work to apply higher-order network analysis to study PM_2.5 transport.

To investigate dual-process persuasion theories in the context of group decision making, we studied low and high need-for-cognition (NFC) participants within a mock trial study. Participants considered plaintiff and defense expert scientific testimony that varied in argument strength. All participants heard a cross-examination of the experts focusing on peripheral information (e.g., credentials) about the expert, but half were randomly assigned to also hear central information highlighting flaws in the expert’s message (e.g., quality of the research presented by the expert). Participants rendered pre- and post-group-deliberation verdicts, which were considered “scientifically accurate” if the verdicts reflected the strong (versus weak) expert message, and “scientifically inaccurate” if they reflected the weak (versus strong) expert message. For individual participants, we replicated studies testing classic persuasion theories: Factors promoting reliance on central information (i.e., central cross-examination, high NFC) improved verdict accuracy because they sensitized individual participants to the quality discrepancy between the experts’ messages. Interestingly, however, at the group level, the more that scientifically accurate mock jurors discussed peripheral (versus central) information about the experts, the more likely their group was to reach the scientifically accurate verdict. When participants were arguing for the scientifically accurate verdict consistent with the strong expert message, peripheral comments increased their persuasiveness, which made the group more likely to reach the more scientifically accurate verdict.

VNP20009 is a very effective anti-cancer agent and can specifically target tumors and inhibit tumor growth. It was assumed that the tumor targeting ability of VNP20009 correlated to its anticancer capacity. However, our observation contradicted to this assumption. Three VNP20009 mutant strains (ΔslyA, ΔSTM3120 and ΔhtrA) with reduced fitness in normal tissues and unchanged fitness in tumors partially or completely lost their anti-cancer capacities. The genes slyA, STM3120 and htrA were required for survival within macrophages and were indispensable for tumor microenvironment remodeling by VNP20009. The infiltration of immune cells occurred less in the tumors of mice infected with the mutant strains. In addition, the mRNA levels of TNF-α and IL-1β were significantly decreased in the tumors of mice treated with the mutant strains. Our results indicate that the immune responses elicited by bacteria rather than the bacterial titer in tumors play a “decisive” role in VNP20009-mediated bacterial cancer therapy, which provides a novel perspective for the underlying mechanism of bacterial cancer therapy.

In order to determine the feasibility of utilizing novel rexinoids for chemotherapeutics and as potential treatments for neurological conditions, we undertook an assessment of the side effect profile of select rexinoid X receptor (RXR) analogs that we reported previously. We assessed pharmacokinetic profiles, lipid and thyroid-stimulating hormone (TSH) levels in rats, and cell culture activity of rexinoids in sterol regulatory element-binding protein (SREBP) induction and thyroid hormone inhibition assays. We also performed RNA sequencing of the brain tissues of rats that had been dosed with the compounds. We show here for the first time that potent rexinoid activity can be uncoupled from drastic lipid changes and thyroid axis variations, and we propose that rexinoids can be developed with improved side effect profiles than the parent compound, bexarotene (1).

Given species inventories of all sites in a planning area, integer programming or heuristic algorithms can prioritize sites in terms of the site's complementary value, that is, the ability of the site to complement (add unrepresented species to) other sites prioritized for conservation. The utility of these procedures is limited because distributions of species are typically available only as coarse atlases or range maps, whereas conservation planners need to prioritize relatively small sites. If such coarse-resolution information can be used to identify small sites that efficiently represent species (i.e., downscaled), then such data can be useful for conservation planning. We develop and test a new type of surrogate for biodiversity, which we call downscaled complementarity. In this approach, complementarity values from large cells are downscaled to small cells, using statistical methods or simple map overlays. We illustrate our approach for birds in Spain by building models at coarse scale (50 × 50 km atlas of European birds, and global range maps of birds interpreted at the same 50 × 50 km grid size), using this model to predict complementary value for 10 × 10 km cells in Spain, and testing how well-prioritized cells represented bird distributions in an independent bird atlas of those 10 × 10 km cells. Downscaled complementarity was about 63–77% as effective as having full knowledge of the 10-km atlas data in its ability to improve on random selection of sites. Downscaled complementarity has relatively low data acquisition cost and meets representation goals well compared with other surrogates currently in use. Our study justifies additional tests to determine whether downscaled complementarity is an effective surrogate for other regions and taxa, and at spatial resolution finer than 10 × 10 km cells. Until such tests have been completed, we caution against assuming that any surrogate can reliably prioritize sites for species representation.

Lack of biodiversity data is a major impediment to prioritizing sites for species representation. Because comprehensive species data are not available in any planning area, planners often use surrogates (such as vegetation communities, or mapped occurrences of a well-inventoried taxon) to prioritize sites. We propose and demonstrate the effectiveness of predicted rarity-weighted richness (PRWR) as a surrogate in situations where species inventories may be available for a portion of the planning area. Use of PRWR as a surrogate involves several steps. First, rarity-weighted richness (RWR) is calculated from species inventories for a q% subset of sites. Then random forest models are used to model RWR as a function of freely available environmental variables for that q% subset. This function is then used to calculate PRWR for all sites (including those for which no species inventories are available), and PRWR is used to prioritize all sites. We tested PRWR on plant and bird datasets, using the species accumulation index to measure efficiency of PRWR. Sites with the highest PRWR represented species with median efficiency of 56% (range 32%–77% across six datasets) when q = 20%, and with median efficiency of 39% (range 20%–63%) when q = 10%. An efficiency of 56% means that selecting sites in order of PRWR rank was 56% as effective as having full knowledge of species distributions in PRWR's ability to improve on the number of species represented in the same number of randomly selected sites. Our results suggest that PRWR may be able to help prioritize sites to represent species if a planner has species inventories for 10%–20% of the sites in the planning area.