Reduction of U(VI) to U(IV) as the result of direct or indirect microbial activity is currently being explored for in situ remediation of subsurface U plumes, under the assumption that U(IV) solubility is controlled by the low-solubility mineral uraninite (U(IV)-dioxide). However, recent characterizations of U in sediments from biostimulated field sites, as well as laboratory U(VI) bioreduction studies, report on the formation of U(IV) species that lack the U═O(2)═U coordination of uraninite, suggesting that phases other than uraninite may be controlling U(IV) solubility in environments with complexing surfaces and ligands. To determine the controls on the formation of such nonuraninite U(IV) species, the current work studied the reduction of carbonate-complexed U(VI) by (1) five Gram-positive Desulfitobacterium strains, (2) the Gram-negative bacteria Anaeromyxobacter dehalogenans 2CP-C and Shewanella putrefaciens CN32, and (3) chemically reduced 9,10-anthrahydroquinone-2,6-disulfonate (AH(2)QDS, a soluble reductant). Further, the effects of 0.3 mM dissolved phosphate on U(IV) species formation were explored. Extended X-ray absorption fine structure (EXAFS) spectroscopy analysis demonstrated that the addition of phosphate causes the formation of a nonuraninite, phosphate-complexed U(IV) species, independent of the biological or abiotic mode of U(VI) reduction. In phosphate-free medium, U(VI) reduction by Desulfitobacterium spp. and by AH(2)QDS resulted in nonuraninite, carbonate-complexed U(IV) species, whereas reduction by Anaeromyxobacter or Shewanella yielded nanoparticulate uraninite. These findings suggest that the Gram-positive Desulfitobacterium strains and the Gram-negative Anaeromyxobacter and Shewanella species use distinct mechanisms to reduce U(VI).

The glycine binding riboswitch forms a unique tandem aptamer structure that binds glycine cooperatively. We employed nucleotide analog interference mapping (NAIM) and mutagenesis to explore the chemical basis of glycine riboswitch cooperativity. Based on the interference pattern, at least two sites appear to facilitate cooperative tertiary interactions, namely, the minor groove of the P1 helix from aptamer 1 and the major groove of the P3a helix from both aptamers. Mutation of these residues altered both the cooperativity and binding affinity of the riboswitch. The data support a model in which the P1 helix of the first aptamer participates in a tertiary interaction important for cooperativity, while nucleotides in the P1 helix of the second aptamer interface with the expression platform. These data have direct analogy to well-characterized mutations in hemoglobin, which provides a framework for considering cooperativity in this RNA-based system.

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Objective To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). Materials and Methods B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared. Results When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8–92.5% vs. 82.1–93.1%; p < 0.001), accuracy (77.9–88.9% vs. 86.2–90.9%; p = 0.038), and positive predictive value (PPV) (60.2–83.3% vs. 70.4–85.2%; p = 0.001). However, there were no significant changes in sensitivity (81.3–88.8% vs. 86.3–95.0%; p = 0.120) and negative predictive value (91.4–93.5% vs. 92.9–97.3%; p = 0.259). Conclusion Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.