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Abstract Background This study explored the significance of consolidation maintenance chemotherapy after concurrent chemoradiotherapy with different regimens in patients with esophageal squamous cell carcinoma. Method A prospective randomized controlled phase III clinical trial was designed and registered in the China Clinical Trials Registry (Registration number: ChiCTR‐TRC‐12002719). Survival data were analyzed in terms of intention‐to‐treat (ITT) and per‐protocol (PP) sets for patients undergoing cisplatin and 5‐fluorouracil (PF) (group A), or cisplatin and paclitaxel (TP) (group B). Results The incidence risk of grade III–IV leukopenia in group B was higher than in group A (49.2% vs. 25.5%, p = 0.012). The survival rates at 1, 2, 3, and 5 years were 83.8%, 62.6%, 53.1%, and 41.3%, respectively. Consolidation chemotherapy after concurrent chemoradiation therapy had no benefit on median progression‐free survival (PFS) (p = 0.95) and overall survival (OS) (p = 0.809). According to the ITT analysis, the median PFS in group A and group B was 28.6 months and 30.3 months (X2 = 0.242, p = 0.623), while the median OS was 31.0 months and 50.3 months (X2 = 1.25，p = 0.263). For the PP analysis, the median PFS in group A and group B were 28.6 months and 30.3 months (p = 0.584), while the median OS was 31.0 months and 50.3 months (p = 0.259), respectively. Patients receiving consolidation chemotherapy did not show significant OS benefits (46.9 months vs. 38.3 months; X2 = 0.059, p = 0.866). Conclusion Similar PFS and OS were found between PF and TP regimens with concurrent chemoradiotherapy. Consolidation chemotherapy did not show any significant OS benefits.

Abstract Cytokinins, which play crucial roles in shoot development, substantially affect grain yield. In rice, the OsRopGEF10-OsRAC3 module is associated with cytokinin signaling and crown root development. However, the effects of RopGEF-mediated cytokinin signaling on rice shoot development and grain yield remain unclear. In this study, we investigated the role of OsRopGEF10 in SAM development and the underlying mechanism. We showed that overexpression of OsRopGEF10 inhibited SAM and panicle development, leading to decreased grain yield. Intriguingly, the overexpression of a specific amino acid mutant of OsRopGEF10, designated gef10-W260S, was found to promote panicle development and grain yield. Further analysis using the BiFC assay revealed that the gef10-W260S mutation disrupted the recruitment of rice histidine phosphotransfer proteins (OsAHP1/2) to the plasma membrane (PM), thereby promoting cytokinin signaling. This effect was corroborated by a dark-induced leaf senescence assay, which revealed an increased cytokinin response in the gef10-W260S ectopic expression lines, whereas the overexpression lines presented a suppressed cytokinin response. Moreover, we revealed that the enhanced panicle development in the gef10-W260S lines was attributable to the upregulated expression of several type-B response regulators (RRs) that are crucial for panicle development. Collectively, these findings revealed the negative regulatory function of OsRopGEF10 in the development of the shoot apical meristem (SAM) via interference with cytokinin signaling. Our study highlights the promising role of OsRopGEF10 as a potential target for regulating SAM and panicle development in rice, revealing a valuable breeding strategy for increasing crop yield.

In order to achieve goat localization to help prevent goats from wandering, we proposed an efficient target localization method based on machine vision. Albas velvet goats from a farm in Ertok Banner, Ordos City, Inner Mongolia Autonomous Region, China, were the main objects of study. First, we proposed detecting the goats using a shallow convolutional neural network, ShallowSE, with the channel attention mechanism SENet, the GeLU activation function and layer normalization. Second, we designed three fully connected coordinate regression network models to predict the spatial coordinates of the goats. Finally, the target detection algorithm and the coordinate regression algorithm were combined to localize the flock. We experimentally confirmed the proposed method using our dataset. The proposed algorithm obtained a good detection accuracy and successful localization rate compared to other popular algorithms. The overall number of parameters in the target detection algorithm model was only 4.5 M. The average detection accuracy reached 95.89% and the detection time was only 8.5 ms. The average localization error of the group localization algorithm was only 0.94 m and the localization time was 0.21 s. In conclusion, the method achieved fast and accurate localization, which helped to rationalize the use of grassland resources and to promote the sustainable development of rangelands.