The simultaneous improvement of grain quality and yield of cereal crops is a major challenge for modern agriculture. Here we show that a rice grain yield quantitative trait locus qLGY3 encodes a MADS-domain transcription factor OsMADS1, which acts as a key downstream effector of G-protein βγ dimers. The presence of an alternatively spliced protein OsMADS1lgy3 is shown to be associated with formation of long and slender grains, resulting in increases in both grain quality and yield potential of rice. The Gγ subunits GS3 and DEP1 interact directly with the conserved keratin-like domain of MADS transcription factors, function as cofactors to enhance OsMADS1 transcriptional activity and promote the co-operative transactivation of common target genes, thereby regulating grain size and shape. We also demonstrate that combining OsMADS1lgy3 allele with high-yield-associated dep1-1 and gs3 alleles represents an effective strategy for simultaneously improving both the productivity and end-use quality of rice.

Roller bearings are some of the most critical and widely used components in rotating machinery. Appearance defect inspection plays a key role in bearing quality control. However, in real industries, bearing defects are usually extremely subtle and have a low probability of occurrence. This leads to distribution discrepancies between the number of positive and negative samples, which makes intelligent data-driven inspection methods difficult to develop and deploy. This paper presents a small data-driven convolution neural network (SDD-CNN) for roller subtle defect inspection via an ensemble method for small data preprocessing. First, label dilation (LD) is applied to solve the problem of an imbalance in class distribution. Second, a semi-supervised data augmentation (SSDA) method is proposed to extend the dataset in a more efficient and controlled way. In this method, a coarse CNN model is trained to generate ground truth class activation and guide the random cropping of images. Third, four variants of the CNN model, namely, SqueezeNet v1.1, Inception v3, VGG-16, and ResNet-18, are introduced and employed to inspect and classify the surface defects of rollers. Finally, a rich set of experiments and assessments is conducted, indicating that these SDD-CNN models, particularly the SDD-Inception v3 model, perform exceedingly well in the roller defect classification task with a top-1 accuracy reaching 99.56%. In addition, the convergence time and classification accuracy for an SDD-CNN model achieve significant improvement compared to that for the original CNN. Overall, using an SDD-CNN architecture, this paper provides a clear path toward a higher precision and efficiency for roller defect inspection in smart manufacturing.

Sirtuin 7 (SIRT7), an NAD +-dependent deacetylase, plays vital roles in energy sensing, but the underlying mechanisms of action remain less clear. Here, we report that SIRT7 is required for p53-dependent cell-cycle arrest during glucose deprivation. We show that SIRT7 directly interacts with p300/CBP-associated factor (PCAF) and the affinity for this interaction increases during glucose deprivation. Upon binding, SIRT7 deacetylates PCAF at lysine 720 (K720), which augments PCAF binding to murine double minute (MDM2), the p53 E3 ubiquitin ligase, leading to accelerated MDM2 degradation. This effect results in upregulated expression of the cell-cycle inhibitor, p21 Waf1/Cip1 , which further leads to cellcycle arrest and decreased cell viability. These data highlight the importance of the SIRT7-PCAF interaction in regulating p53 activity and cell-cycle progression during conditions of glucose deprivation. This axis may represent a new avenue to design effective therapeutics based on tumor starvation.