Requirements for mammalian promoters to decode transcription factor dynamics.

In response to different stimuli many transcription factors (TFs) display different activation dynamics that trigger the expression of specific sets of target genes, suggesting that promoters have a way to decode dynamics. Here, we use optogenetics to directly manipulate the nuclear localization of a synthetic TF in mammalian cells without affecting other processes. We generate pulsatile or sustained TF dynamics and employ live cell microscopy and mathematical modelling to analyse the behaviour of a library of reporter constructs. We find decoding of TF dynamics occurs only when the coupling between TF binding and transcription pre-initiation complex formation is inefficient and that the ability of a promoter to decode TF dynamics gets amplified by inefficient translation initiation. Using the knowledge acquired, we build a synthetic circuit that allows obtaining two gene expression programs depending solely on TF dynamics. Finally, we show that some of the promoter features identified in our study can be used to distinguish natural promoters that have previously been experimentally characterized as responsive to either sustained or pulsatile p53 and NF-κB signals. These results help elucidate how gene expression is regulated in mammalian cells and open up the possibility to build complex synthetic circuits steered by TF dynamics.

Author Correction: U-Net: deep learning for cell counting, detection, and morphometry.

In the version of this paper originally published, one of the affiliations for Dominic Mai was incorrect: "Center for Biological Systems Analysis (ZBSA), Albert-Ludwigs-University, Freiburg, Germany" should have been "Life Imaging Center, Center for Biological Systems Analysis, Albert-Ludwigs-University, Freiburg, Germany." This change required some renumbering of subsequent author affiliations. These corrections have been made in the PDF and HTML versions of the article, as well as in any cover sheets for associated Supplementary Information.

U-Net: deep learning for cell counting, detection, and morphometry.

U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.

Closed-loop experimental optimization of tunable lenses.

Tunable lenses (TLs) are optical devices that can change their optical power in response to an electrical signal. In many applications, they are often pushed to or beyond their temporal limits. Fast periodic and/or abrupt variations of the optical power induce undesired distortions in their transient response and produce a decrease in their performance. A low-cost focimetry system, along with a custom closed-loop iterative optimization algorithm, was developed to (1) characterize a TL's response at high speed and (2) optimize their performance in realistic TL working conditions. A significant lens performance improvement was found in about 23 iterations with a decrease in the area under the error curve and an improved effective time. Applying the closed-loop optimization algorithm in a depth scanning experiment enhanced the image quality. Quantitatively, the image quality was evaluated using the structural similarity index metric that improves in individual frames, on average, from 0.345 to 0.895.

Tunable lenses: dynamic characterization and fine-tuned control for high-speed applications.

Tunable lenses are becoming ubiquitous, in applications including microscopy, optical coherence tomography, computer vision, quality control, and presbyopic corrections. Many applications require an accurate control of the optical power of the lens in response to a time-dependent input waveform. We present a fast focimeter (3.8 KHz) to characterize the dynamic response of tunable lenses, which was demonstrated on different lens models. We found that the temporal response is repetitive and linear, which allowed the development of a robust compensation strategy based on the optimization of the input wave, using a linear time-invariant model. To our knowledge, this work presents the first procedure for a direct characterization of the transient response of tunable lenses and for compensation of their temporal distortions, and broadens the potential of tunable lenses also in high-speed applications.

Experimental characterization, modelling and compensation of temperature effects in optotunable lenses.

Most tunable lenses (TLs) are affected by deviations in optical power induced by external temperature changes or due to internal heating while in use. This study proposes: (1) An experimental characterization method to evaluate the magnitude of the optical power deviations due to internal temperature shifts; (2) three different mathematical models (experimental, polynomial, and optimized) to describe the response of the lens with temperature; (3) predictions of the internal temperature shifts while using the lens in time frames of minutes, seconds, and milliseconds and; (4) a real time optical power compensation tool based on the implementation of the models on a custom voltage electronic driver. The compensation methods were successfully applied to two TL samples in static and dynamic experiments and in hysteresis cycles. After 40 min at a static nominal power of 5 diopters (dpt), the internal temperature exponentially increased by 17 °C, producing an optical power deviation of 1.0 dpt (1.5 dpt when the lens cools down), representing a 20% distortion for heating and 30% for cooling. Modelling and compensation reduced the deviations to 0.2 dpt when heating (0.35 dpt when cooling) and the distortions to 4% and 7%. Similar levels of improvement were obtained in dynamic and hysteresis experiments. Compensation reduced temperature effects by more than 75%, representing a significant improvement in the performance of the lens.

Experimental validations of a tunable-lens-based visual demonstrator of multifocal corrections.

The Simultaneous Vision simulator (SimVis) is a visual demonstrator of multifocal lens designs for prospective intraocular lens replacement surgery patients and contact lens wearers. This programmable device employs a fast tunable lens and works on the principle of temporal multiplexing. The SimVis input signal is tailored to mimic the optical quality of the multifocal lens using the theoretical SimVis temporal profile, which is evaluated from the through-focus Visual Strehl ratio metric of the multifocal lens. In this paper, for the first time, focimeter-verified on-bench validations of multifocal simulations using SimVis are presented. Two steps are identified as being critical to accurate SimVis simulations. Firstly, a new iterative approach is presented that improves the accuracy of the theoretical SimVis temporal profile for three different multifocal intraocular lens designs - diffractive trifocal, refractive segmented bifocal, and refractive extended depth of focus, while retaining a low sampling. Secondly, a fast focimeter is used to measure the step response of the tunable lens, and the input signal is corrected to include the effects of the transient behavior of the tunable lens. It was found that the root-mean-square of the difference between the estimated through-focus Visual Strehl ratio of the multifocal lens and SimVis is not greater than 0.02 for all the tested multifocal designs.