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Recently, we reported that zinc regulates gliotoxin biosynthesis via ZafA, which is a zinc-responsive transcriptional activator. From an HPLC analysis of culture media of Aspergillus fumigatus, we found a trend of decreasing gliotoxin production but increasing pseurotin A and fumagillin production in proportion to the zinc concentration. The expression of the genes involved in pseurotin A biosynthesis was upregulated under high zinc concentrations. Furthermore, upregulated expression of pseurotin A biosynthetic genes and higher production of pseurotin A were observed in the zafA deletion strain. Interestingly, the deletion of gliZ, a transcriptional activator of gliotoxin biosynthesis genes, resulted in upregulated expression of pseurotin A biosynthetic genes and increased production of pseurotin A. We detected upregulation of fumR expression in the gliZ and zafA deletion mutants. The overexpression of gliZ observed in the zafA deletion mutant resulted in the failure of the mutant to increase pseurotin A production, which is a phenotype of the zafA deletion mutant. These results suggest that ZafA sequentially regulates pseurotin A biosynthesis through GliZ. Finally, we found through a murine virulence test that the gliZ and fumR double-deletion mutants showed a delayed death rate compared with the single-deletion mutants of either gliZ or fumR. Taken together, these results suggested that the biosynthesis of gliotoxin and pseurotin A are regulated in opposite ways by zinc utilization and that each secondary metabolite is synthesized when the synthesis of another secondary metabolite fails to protect it against the defense system of the host.

Background To assess pulmonary arteriovenous malformation (PAVM) recanalization after embolization based on PAVM diameter changes on computed tomography (CT), with pulmonary angiography used as a gold standard. Methods A retrospective review was done of patients from 2008 to 2019 with a PAVM treated with endovascular embolization. The treatment outcome was determined by conventional angiography. Follow-up pulmonary angiography was performed when recanalization was suspected on CT, or embolization of all lesions in multiple PAVM patients could not be completed in a single session. Patients who had no preprocedural or follow-up CT were excluded. Draining vein, feeding artery, and venous sac diameter were measured on CT, and diameter reduction rates were compared with the widely-used, binary 70 % criteria. Results Forty-one patients with 114 PAVMs were treated during the study period. Eight patients with 50 PAVMs met the inclusion criteria. Mean vein, artery, and venous sac diameter reduction rates were as follows: 59.2 ± 9.3 %, 47.5 ± 10.6 %, and 62.6 ± 13.2 %, respectively, in the occluded group and 5.4 ± 19.5 %, 11.3 ± 17.7 %, and 26.8 ± 14.2 %, respectively, in the recanalized group. The area under the receiver operating characteristic curves for PAVM recanalization for the draining vein was 1.00, showing a better result than the artery (0.97) and sac (0.99). Patients showed > 42 % draining vein diameter reduction in the occluded group and Conclusions The widely-used 70 % binary criteria showed limited performance in predicting outcomes in this angiographically-confirmed case series. Further investigations are warranted to establish a strategy for detecting recanalization after PAVM embolization.

2-(2,5-Dimethoxy-4-nitrophenyl)-N-(2-methoxybenzyl)ethanamine (25N-NBOMe, 2C-N-NBOMe, NBOMe-2C-N) is a novel synthetic psychoactive substance of the phenethylamine chemical class. A few metabolism studies have been conducted for 25I-NBOMe, 25B-NBOMe, and 25C-NBOMe, and others, whereas 25N-NBOMe metabolism has not been researched. In this study, the in vitro metabolism of 25N-NBOMe was investigated with human liver microsomes, and the reaction mixture was analyzed using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q-TOF/MS). Formation of 14 metabolites (M1-M14) was yielded with incubation of 25N-NBOMe in human liver microsomes in the presence of NADPH. The metabolites were structurally characterized on the basis of accurate mass analysis and MS/MS fragmentation patterns. The biotransformations included hydroxylation, O-demethylation, N-dealkylation, nitro reduction, dehydrogenation, carbonylation, and combinations thereof. Hydroxyl metabolite was the most abundant compound after the phase I process. These results provide helpful information establishing biomarkers in case of 25N-NBOMe ingestion.

With the recent advancement of data acquisition techniques, 3D animation is becoming a new challenging subject for data processing. In this paper, we present a joint entropy-based key-frame extraction method, which further derives a motion segmentation method for 3D animations. We start by applying an existing deformation-based feature descriptor to measure the degree of deformation of each triangle within each frame, from which we compute the statistical joint probability distribution of triangles’ deformation between two consecutive subsequences of frames with a fixed length. Then, we further compute joint entropy between the two subsequences. This allows us to extract key-frames by taking the local maximal from the joint entropy curve (or energy curve) of a given 3D animation. Furthermore, we classify the extracted key-frames by grouping the key-frames with similar motions into the same cluster. Finally, we compute a boundary frame between each of the two neighboring frames with different motions, which is achieved by minimizing the variance of energy between the two motions. The experimental results show that our method successfully extracts representative key-frames of different motions, and the comparisons with existing methods show the effectiveness and the efficiency of our method.

International audience; Although there have been a large body of works on computing the similarity of static shapes, similarity judgments on deforming meshes are not studied well. In this study, we investigate a similarity measurement method for comparing two deforming meshes. Based on the degree of deformation, we first binarily label each triangle within each frame as either 'deformed' or 'rigid', then merge the 'deformed' triangles in both spatial and temporal domains for the segmentation. The segmentation results are encoded in a form of evolving graph, with an aim of obtaining a compact representation of the motion of the mesh. Finally, we formulate the similarity measurement as a sequence matching problem: after clustering similar graphs and assigning each of the graphs with the cluster labels, each deforming mesh is represented with a sequence of labels. Then, we apply a sequence alignment algorithm to compute the locally optimal alignment between the two label sequences, and to compute the similarity by normalizing the alignment score. The experimental results over several datasets show that the similarities of animation data can be captured correctly using our approach. This may be significant, as it solves a problem that cannot be handled by current approaches.

Although automatic feature detection has been a long-sought subject by researchers in computer graphics and computer vision, feature extraction on deforming models remains a relatively unexplored area. In this paper, we develop a new method for automatic detection of spatio-temporal feature points on animated meshes. Our algorithm consists of three main parts. We first define local deformation characteristics, based on strain and curvature values computed for each point at each frame. Next, we construct multi-resolution space---time Gaussians and difference-of-Gaussian (DoG) pyramids on the deformation characteristics representing the input animated mesh, where each level contains 3D smoothed and subsampled representation of the previous level. Finally, we estimate locations and scales of spatio-temporal feature points by using a scale-normalized differential operator. A new, precise approximation of spatio-temporal scale-normalized Laplacian has been introduced, based on the space---time DoG. We have experimentally verified our algorithm on a number of examples and conclude that our technique allows to detect spatio and temporal feature points in a reliable manner.