Internal organs are asymmetrically positioned inside the body. Embryonic motile cilia play an essential role in this process by generating a directional fluid flow inside the vertebrate left-right organizer. Detailed characterization of how fluid flow dynamics modulates laterality is lacking. We used zebrafish genetics to experimentally generate a range of flow dynamics. By following the development of each embryo, we show that fluid flow in the left-right organizer is asymmetric and provides a good predictor of organ laterality. This was tested in mosaic organizers composed of motile and immotile cilia generated by dnah7 knockdowns. In parallel, we used simulations of fluid dynamics to analyze our experimental data. These revealed that fluid flow generated by 30 or more cilia predicts 90% situs solitus, similar to experimental observations. We conclude that cilia number, dorsal anterior motile cilia clustering, and left flow are critical to situs solitus via robust asymmetric charon expression.

In autosomal dominant polycystic kidney disease (ADPKD), cyst inflation and continuous enlargement are associated with marked transepithelial ion and fluid secretion into the cyst lumen via cystic fibrosis transmembrane conductance regulator (CFTR). Indeed, the inhibition or degradation of CFTR prevents the fluid accumulation within cysts. The in vivo mechanisms by which the lack of Polycystin-2 leads to CFTR stimulation are an outstanding challenge in ADPKD research and may bring important biomarkers for the disease. However, hampering their study, the available ADPKD in vitro cellular models lack the three-dimensional architecture of renal cysts and the ADPKD mouse models offer limited access for live-imaging experiments in embryonic kidneys. Here, we tested the zebrafish Kupffer's vesicle (KV) as an alternative model-organ. KV is a fluid-filled vesicular organ, lined by epithelial cells that express both CFTR and Polycystin-2 endogenously, being each of them easily knocked-down. Our data on the intracellular distribution of Polycystin-2 support its involvement in the KV fluid-flow induced Ca2+-signalling. Mirroring kidney cysts, the KV lumen inflation is dependent on CFTR activity and, as we clearly show, the knockdown of Polycystin-2 results in larger KV lumens through overstimulation of CFTR. In conclusion, we propose the zebrafish KV as a model organ to study the renal cyst inflation. Favouring its use, KV volume can be easily determined by in vivo imaging offering a live readout for screening compounds and genes that may prevent cyst enlargement through CFTR inhibition.

Foxj1a is necessary and sufficient to specify motile cilia. Using transcriptional studies and slow-scan two-photon live imaging capable of identifying the number of motile and immotile cilia, we now established that the final number of motile cilia depends on Notch signalling (NS). We found that despite all left-right organizer (LRO) cells express foxj1a and the ciliary axonemes of these cells have dynein arms, some cilia remain immotile. We identified that this decision is taken early in development in the Kupffer’s Vesicle (KV) precursors the readout being her12 transcription. We demonstrate that overexpression of either her12 or Notch intracellular domain (NICD) increases the number of immotile cilia at the expense of motile cilia, and leads to an accumulation of immotile cilia at the anterior half of the KV. This disrupts the normal fluid flow intensity and pattern, with consequent impact on dand5 expression pattern and left-right (L-R) axis establishment.

Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networksspecifically twittercan improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment.

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