Grain yield in bread wheat (Triticum aestivum L.) is largely determined by inflorescence architecture. Zang734 is an endemic Tibetan wheat variety that exhibits a rare triple spikelet (TRS) phenotype with significantly increased spikelet/floret number per spike. However, the molecular basis underlying this specific spike morphology is completely unknown.Through map-based cloning, the causal genes for TRS trait in Zang734 were isolated. Furthermore, using CRISPR/Cas9-based gene mutation, transcriptome sequencing and proteinprotein interaction, the downstream signalling networks related to spikelet formation and awn elongation were defined.Results showed that the null mutation in WFZP-A together with deletion of WFZP-D led to the TRS trait in Zang734. More interestingly, WFZP plays a dual role in simultaneously repressing spikelet formation gene TaBA1 and activating awn development genes, basically through the recruitments of chromatin remodelling elements and the Mediator complex.Our findings provide insights into the molecular bases by which WFZP suppresses spikelet formation but promotes awn elongation and, more importantly, define WFZP-D as a favourable gene for high-yield crop breeding.

A suitable imaging condition is critical for the success of seismic imaging or source location. To understand what imaging condition to select for handling noisy data, the antinoise performance of the maximum amplitude imaging condition (MAIC), the autocorrelation imaging condition (ACIC), and the geometric mean imaging condition (GMIC) were comparatively studied. Synthetic microseismic data based on the Marmousi2 model, with different levels of synthetic Gaussian noise and field noise separately added, were used for tests. For Gaussian noise data, five signal-to-noise (S/N) ratio levels were considered, ranging from an absolutely clean level of [Formula: see text] to an extremely noisy level of [Formula: see text], in an increment of five times of the lower level of S/N. It was found that the antinoise ability of MAIC outperforms ACIC, and ACIC outperforms GMIC. This conclusion was confirmed to be valid for field noise in the further experiments performed, using 16 groups of industrial noise recordings from different areas. The statistical analysis shows these performance differences are statistically consistently significant. In terms of spatial resolution, it is the other way around; that is, GMIC outperforms ACIC, and ACIC outperforms MAIC. These suggest that in choosing a suitable imaging condition for time-reverse imaging location, one needs to consider the balance between the resolution demand and data quality requirement. If the data quality is very high, GMIC may be used to achieve a high-resolution location result. Conversely, if the data quality is poor, MAIC is a good choice for obtaining a robust location result. In between, ACIC or grouped GMIC is a proper approach to work out a balanced result for resolution demand and the noisy level provision.

Precise source depths are critical to the understanding and interpretation of many seismological and tectonic processes. However, conventional earthquake location methods based on the arrival times of direct P and S phases have far less constraint on focal depths than on epicenters. Therefore, developing a method that can systematically and efficiently estimate focal depths with high accuracy, especially for the vast number of small earthquakes that can only be observed at local and regional distances, would make significant contributions to the geoscience research community. In this study, we develop a new method, named “Depth‐Scanning Algorithm,” to efficiently identify depth phases at local and regional distances. We first construct template waveforms of possible depth phases by applying various phase shifts to the original P and S waveforms to mimic the effect of reflection(s). We then systematically scan waveforms after the P and S phases for segments that match the depth‐phase templates. The arrival times of those segments are compared to the theoretical arrival times of depth phases predicted with an assumed velocity model and focal depth. We repeat the above process for a range of assumed focal depths, and the one most consistent with the theoretical prediction is deemed the final solution. Synthetic tests and applications to real data demonstrate the merits of our method compared to conventional location methods.