The wheat basic helix-loop-helix gene TabHLH123 positively modulates the formation of crown roots and is associated with plant height and 1000-grain weight under various conditions.

Crown roots are the main components of the fibrous root system in cereal crops and play critical roles in plant adaptation; however, the molecular mechanisms underlying their formation in wheat (Triticum aestivum) have not been fully elucidated. In this study, we identified a wheat basic helix-loop-helix (bHLH) protein, TabHLH123, that interacts with the essential regulator of crown root initiation, MORE ROOT in wheat (TaMOR). TabHLH123 is expressed highly in shoot bases and roots. Ectopic expression of TabHLH123 in rice resulted in more roots compared with the wild type. TabHLH123 regulates the expression of genes controlling crown-root development and auxin metabolism, responses, and transport. In addition, we analysed the nucleotide sequence polymorphisms of TabHLH123s in the wheat genome and identified a superior haplotype, TabHLH123-6B, that is associated with high root dry weight and 1000-grain weight, and short plant height. Our study reveals the role of TabHLH123 in controlling the formation of crown roots and provides beneficial insights for molecular marker-assisted breeding in wheat.

EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control.

The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have implemented various measures and policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the spread of the epidemic. These countermeasures seek to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policymakers have expressed the urgency to effectively evaluate the outcome of human restriction policies with the aid of big data and information technology. Thus, based on big human mobility data and city POI data, an interactive visual analytics system called Epidemic Mobility (EpiMob) was designed in this study. The system interactively simulates the changes in human mobility and infection status in response to the implementation of a certain restriction policy or a combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies. Then, the results reflecting the infection situation under different policies are dynamically displayed and can be flexibly compared and analyzed in depth. Multiple case studies consisting of interviews with domain experts were conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area) to demonstrate that the system can provide insight into the effects of different human mobility restriction policies for epidemic control, through measurements and comparisons.

TaMOR is essential for root initiation and improvement of root system architecture in wheat.

Optimal root system architecture is beneficial for water-fertilizer use efficiency, stress tolerance and yield improvement of crops. However, because of the complexity of root traits and difficulty in phenotyping deep roots, the study on mechanisms of root development is rarely reported in wheat (Triticum aestivum L.). In this study, we identified that the LBD (LATERAL ORGAN BOUNDARIES DOMAIN) gene TaMOR (MORE ROOT in wheat) determines wheat crown root initiation. The mor mutants exhibited less or even no crown root, dwarfism, less grain number and lodging caused by few roots. The observation of cross sections showed that crown root initiation is inhibited in the mor mutants. Molecular assays revealed that TaMOR interacts with the auxin response factor ARF5 to directly induce the expression of the auxin transporter gene PIN2 (PIN-FORMED 2) in the root base to regulate crown root initiation. In addition, a 159-bp MITE (miniature inverted-repeat transposable element) insertion causing DNA methylation and lower expression of TaMOR-B was identified in TaMOR-B promoter, which is associated with lower root dry weight and shorter plant height. The results bring new light into regulation mechanisms of crown root initiation and offer a new target for the improvement of root system architecture in wheat.

Discovering Key Sub-Trajectories to Explain Traffic Prediction.

Flow prediction has attracted extensive research attention; however, achieving reliable efficiency and interpretability from a unified model remains a challenging problem. In the literature, the Shapley method offers interpretable and explanatory insights for a unified framework for interpreting predictions. Nevertheless, using the Shapley value directly in traffic prediction results in certain issues. On the one hand, the correlation of positive and negative regions of fine-grained interpretation areas is difficult to understand. On the other hand, the Shapley method is an NP-hard problem with numerous possibilities for grid-based interpretation. Therefore, in this paper, we propose Trajectory Shapley, an approximate Shapley approach that functions by decomposing a flow tensor input with a multitude of trajectories and outputting the trajectories' Shapley values in a specific region. However, the appearance of the trajectory is often random, leading to instability in interpreting results. Therefore, we propose a feature-based submodular algorithm to summarize the representative Shapley patterns. The summarization method can quickly generate the summary of Shapley distributions on overall trajectories so that users can understand the mechanisms of the deep model. Experimental results show that our algorithm can find multiple traffic trends from the different arterial roads and their Shapley distributions. Our approach was tested on real-world taxi trajectory datasets and exceeded explainable baseline models.

FedVoting: A Cross-Silo Boosting Tree Construction Method for Privacy-Preserving Long-Term Human Mobility Prediction.

The prediction of human mobility can facilitate resolving many kinds of urban problems, such as reducing traffic congestion, and promote commercial activities, such as targeted advertising. However, the requisite personal GPS data face privacy issues. Related organizations can only collect limited data and they experience difficulties in sharing them. These data are in "isolated islands" and cannot collectively contribute to improving the performance of applications. Thus, the method of federated learning (FL) can be adopted, in which multiple entities collaborate to train a collective model with their raw data stored locally and, therefore, not exchanged or transferred. However, to predict long-term human mobility, the performance and practicality would be impaired if only some models were simply combined with FL, due to the irregularity and complexity of long-term mobility data. Therefore, we explored the optimized construction method based on the high-efficient gradient-boosting decision tree (GBDT) model with FL and propose the novel federated voting (FedVoting) mechanism, which aggregates the ensemble of differential privacy (DP)-protected GBDTs by the multiple training, cross-validation and voting processes to generate the optimal model and can achieve both good performance and privacy protection. The experiments show the great accuracy in long-term predictions of special event attendance and point-of-interest visits. Compared with training the model independently for each silo (organization) and state-of-art baselines, the FedVoting method achieves a significant accuracy improvement, almost comparable to the centralized training, at a negligible expense of privacy exposure.