Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds to thousands of individual neurons. An emerging technical challenge that parallels the advancement in imaging a large number of individual neurons is the processing of correspondingly large datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semimanual inspection, which cannot be scaled for processing large datasets. To address this challenge, we focused on developing an automated segmentation method, which we refer to as automated cell segmentation by adaptive thresholding (ACSAT). ACSAT works with a time-collapsed image and includes an iterative procedure that automatically calculates global and local threshold values during successive iterations based on the distribution of image pixel intensities. Thus, the algorithm is capable of handling variations in morphological details and in fluorescence intensities in different calcium imaging datasets. In this paper, we demonstrate the utility of ACSAT by testing it on 500 simulated datasets, two wide-field hippocampus datasets, a wide-field striatum dataset, a wide-field cell culture dataset, and a two-photon hippocampus dataset. For the simulated datasets with truth, ACSAT achieved >80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.

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A voltage-controlled color-tunable and high-efficiency organic light-emitting diode (OLED) by inserting 16-nm N,N′-dicarbazolyl-3,5-benzene (mCP) interlayer between two complementary emitting layers (EMLs) was fabricated. The OLED emitted multicolor ranging from blue (77.4 cd/A @ 6 V), white (70.4 cd/A @ 7 V), to yellow (33.7 cd/A @ 9 V) with voltage variation. An equivalent model was proposed to reveal the color-tunable and high-efficiency emission of OLEDs, resulting from the swing of exciton bilateral migration zone near mCP/blue-EML interface. Also, the model was verified with a theoretical arithmetic using single-EML OLEDs to disclose the crucial role of mCP exciton adjusting layer.

Payment management is an operations management challenge in online peer‐to‐peer (P2P) lending. It is critical to control the cost of debt collection incurred in late repayments and defaults. We study whether and how a platform can leverage on borrowers’ social connections and use automatic social notifications in regulating repayment behavior. In collaboration with a large online P2P lending platform, we conduct a randomized field experiment to investigate the effect of social notifications targeted at different contact groups (core‐circle and peripheral‐circle groups). Our results indicate that notifying social contacts of a delinquent regarding the overdue payment significantly improves the repayment rate. Compared with the control group in which no notification messages are sent to social contacts, notification‐triggered social sanctions and social support reduce the default rate by more than 50% in both core‐circle and peripheral‐circle groups. Furthermore, we find that social notifications targeted at peripheral‐circle social contacts are only effective in the short term, and its effectiveness decreases with repeated use. By contrast, social notifications targeted at core‐circle social contacts have a lasting effect.

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