Effects of molting on the expression of ecdysteroid responsive genes in the crustacean molting gland (Y-organ).

Ecdysteroid molting hormones coordinate arthropod growth and development. Binding of 20-hydroxyecdysone (20E) to ecdysteroid receptor EcR/RXR activates a cascade of nuclear receptor transcription factors that mediate tissue responses to hormone. Insect ecdysteroid responsive and Forkhead box class O (FOXO) transcription factor gene sequences were used to extract orthologs from blackback land crab (Gecarcinus lateralis) Y-organ (YO) transcriptome: Gl-Ecdysone Receptor (EcR), Gl-Broad Complex (Br-C), Gl-E74, Gl-Hormone Receptor 3 (HR3), Gl-Hormone Receptor 4 (HR4), Gl-FOXO, and Gl-Fushi tarazu factor-1 (Ftz-f1). Quantitative polymerase chain reaction quantified mRNA levels in tissues from intermolt animals and in YO of animals induced to molt by multiple limb autotomy (MLA) or eyestalk ablation (ESA). Gl-EcR, Gl-Retinoid X Receptor (RXR), Gl-Br-C, Gl-HR3, Gl-HR4, Gl-E74, Gl-E75, Gl-Ftz-f1, and Gl-FOXO were expressed in all 10 tissues, with Gl-Br-C, Gl-E74, Gl-E75, and Gl-HR4 mRNA levels in the YO lower than those in most of the other tissues. In MLA animals, molting had no effect on Gl-Br-C, Gl-E74, and Gl-Ftz-f1 mRNA levels and little effect on Gl-EcR, Gl-E75, and Gl-HR4 mRNA levels. Gl-HR3 and Gl-FOXO mRNA levels were increased during premolt stages, while Gl-RXR mRNA level was highest during intermolt and premolt stages and lowest at postmolt stage. In ESA animals, YO mRNA levels were not correlated with hemolymph ecdysteroid titers. ESA had no effect on Gl-EcR, Gl-E74, Gl-HR3, Gl-HR4, Gl-Ftz-f1, and Gl-FOXO mRNA levels, while Gl-RXR, Gl-Br-C, and Gl-E75 mRNA levels were decreased at 3 days post-ESA. These data suggest that transcriptional up-regulation of Gl-FOXO and Gl-HR3 contributes to increased YO ecdysteroidogenesis during premolt. By contrast, transcriptional regulation of ecdysteroid responsive genes and ecdysteroidogenesis were uncoupled in the YO of ESA animals.

Deep Learning-Derived Myocardial Strain.

BACKGROUND: Echocardiographic strain measurements require extensive operator experience and have significant intervendor variability. Creating an automated, open-source, vendor-agnostic method to retrospectively measure global longitudinal strain (GLS) from standard echocardiography B-mode images would greatly improve post hoc research applications and may streamline patient analyses. OBJECTIVES: This study was seeking to develop an automated deep learning strain (DLS) analysis pipeline and validate its performance across multiple applications and populations. METHODS: Interobserver/-vendor variation of traditional GLS, and simulated effects of variation in contour on speckle-tracking measurements were assessed. The DLS pipeline was designed to take semantic segmentation results from EchoNet-Dynamic and derive longitudinal strain by calculating change in the length of the left ventricular endocardial contour. DLS was evaluated for agreement with GLS on a large external dataset and applied across a range of conditions that result in cardiac hypertrophy. RESULTS: In patients scanned by 2 sonographers using 2 vendors, GLS had an intraclass correlation of 0.29 (95% CI: -0.01 to 0.53, P = 0.03) between vendor measurements and 0.63 (95% CI: 0.48-0.74, P < 0.001) between sonographers. With minor changes in initial input contour, step-wise pixel shifts resulted in a mean absolute error of 3.48% and proportional strain difference of 13.52% by a 6-pixel shift. In external validation, DLS maintained moderate agreement with 2-dimensional GLS (intraclass correlation coefficient [ICC]: 0.56, P = 0.002) with a bias of -3.31% (limits of agreement: -11.65% to 5.02%). The DLS method showed differences (P < 0.0001) between populations with cardiac hypertrophy and had moderate agreement in a patient population of advanced cardiac amyloidosis: ICC was 0.64 (95% CI: 0.53-0.72), P < 0.001, with a bias of 0.57%, limits of agreement of -4.87% to 6.01% vs 2-dimensional GLS. CONCLUSIONS: The open-source DLS provides lower variation than human measurements and similar quantitative results. The method is rapid, consistent, vendor-agnostic, publicly released, and applicable across a wide range of imaging qualities.

HERMES : Heuristic emergency response management expert system

HERMES, a prototype expert system to provide advice and assistance in the management of emergencies involving hazardous materials, was developed by the Alberta Research Council, Alberta Public Safety Services, and Emergency Preparedness Canada. This system was implemented on the symbolics artificial inteligence workstation, using the ART (AutomatedReasoning Tool) software. (AU)