ALOHA with Queue Sharing (ALOHA-QS)  maintains most of the simplicity of ALOHA  with priority acknowledgments  (ACK) and attains the high throughput of transmission scheduling methods that  require  clock synchronization.  Channel access with ALOHA-QS  consists of a sequence of queue cycles, with each cycle having one or multiple  collision-free transmissions by nodes that have joined the transmission queue and  a  single request turn to join the queue.    The signaling  of ALOHA-QS  entails adding to  packet headers the size of the shared queue, the position of the sending node in the  queue,  a bit indicating the end of transmissions by the transmitting node, and a bit stating whether or not a new node joined the queue successfully.    The throughput of ALOHA-QS is compared with the throughput of  TDMA with a fixed transmission schedule, ALOHA with priority ACK's,  and CSMA with priority ACK's analytically and by simulation.

Deep learning is transforming bioimage analysis, but its application in single-cell segmentation is limited by the lack of large, diverse annotated datasets. We addressed this by introducing a CycleGAN-based architecture, cGAN-Seg, that enhances the training of cell segmentation models with limited annotated datasets. During training, cGAN-Seg generates annotated synthetic phase-contrast or fluorescent images with morphological details and nuances closely mimicking real images. This increases the variability seen by the segmentation model, enhancing the authenticity of synthetic samples and thereby improving predictive accuracy and generalization. Experimental results show that cGAN-Seg significantly improves the performance of widely used segmentation models over conventional training techniques. Our approach has the potential to accelerate the development of foundation models for microscopy image analysis, indicating its significance in advancing bioimage analysis with efficient training methodologies.

Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell level with high temporal resolution. Successful application of single-cell time-lapse microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. However, segmentation and tracking of single cells remain challenging for the analysis of time-lapse microscopy images, in particular for widely available and non-toxic imaging modalities such as phase-contrast imaging. This work presents a versatile and trainable deep-learning model, termed DeepSea, that allows for both segmentation and tracking of single cells in sequences of phase-contrast live microscopy images with higher precision than existing models. We showcase the application of DeepSea by analyzing cell size regulation in embryonic stem cells.