A PAR-1–mediated bias in microtubule organization in the Drosophila oocyte underlies posterior-directed mRNA transport.

Intercellular communication is commonly mediated by the regulated fusion, or exocytosis, of vesicles with the cell surface. SNARE (soluble N-ethymaleimide sensitive factor attachment protein receptor) proteins are the catalytic core of the secretory machinery, driving vesicle and plasma membrane merger. Plasma membrane SNAREs (tSNAREs) are proposed to reside in dense clusters containing many molecules, thus providing a concentrated reservoir to promote membrane fusion. However, biophysical experiments suggest that a small number of SNAREs are sufficient to drive a single fusion event. Here we show, using molecular imaging, that the majority of tSNARE molecules are spatially separated from secretory vesicles. Furthermore, the motilities of the individual tSNAREs are constrained in membrane micro-domains, maintaining a non-random molecular distribution and limiting the maximum number of molecules encountered by secretory vesicles. Together our results provide a new model for the molecular mechanism of regulated exocytosis and demonstrate the exquisite organization of the plasma membrane at the level of individual molecular machines.

No abstract

Fluorescence imaging of dynamical processes in live cells often results in a low signal-to-noise ratio. We present a novel feature-preserving non-local means approach to denoise such images to improve feature recovery and particle detection. The commonly used non-local means filter is not optimal for noisy biological images containing small features of interest because image noise prevents accurate determination of the correct coefficients for averaging, leading to over-smoothing and other artifacts. Our adaptive method addresses this problem by constructing a particle feature probability image, which is based on Haar-like feature extraction. The particle probability image is then used to improve the estimation of the correct coefficients for averaging. We show that this filter achieves higher peak signal-to-noise ratio in denoised images and has a greater capability in identifying weak particles when applied to synthetic data. We have applied this approach to live-cell images resulting in enhanced detection of end-binding-protein 1 foci on dynamically extending microtubules in photo-sensitive Drosophila tissues. We show that our feature-preserving non-local means filter can reduce the threshold of imaging conditions required to obtain meaningful data.

To evaluate the impact of thyroid nodule sizes on the diagnostic performance of thyroid imaging reporting and data system (TIRADS) and ultrasound patterns of 2015 American Thyroid Association (ATA) guidelines. Total 734 patients with 962 thyroid nodules were recruited in this retrospective study. All nodules were divided into three groups according to the maximal diameter (d < 10 mm, d = 10–20 mm and d > 20 mm). The ultrasound images were categorized based on TIRADS and ATA ultrasound patterns, respectively. A total of 931 (96.8%) and 906 (94.2%) patterns met the criteria for TIRADS and ATA ultrasound patterns. The AUC (0.849) and sensitivity (85.3%) of TIRADS were highest in d = 10–20 mm group. However, ATA had highest AUC (0.839) and specificity (89.8%) in d > 20 mm group. ATA ultrasound patterns had higher specificity (P = 0.04), while TI-RADS had higher sensitivity (P = 0.02). In nodules d > 20 mm, the specificity of ATA patterns was higher than TIRADS (P = 0.003). Our results indicated that nodule sizes may influence the diagnostic performance of TIRADS and ATA ultrasound patterns. The ATA patterns may yield higher specificity than TIRADS, especially in nodules larger than 20 mm.