During vertebrate egg maturation, cytokinesis initiates after one pole of the bipolar metaphase I spindle attaches to the oocyte cortex, resulting in the formation of a polar body and the mature egg. It is not known what signal couples the spindle pole positioning to polar body formation. We approached this question by drawing an analogy to mitotic exit in budding yeast, as asymmetric spindle attachment to the appropriate cortical region is the common regulatory cue. In budding yeast, the small G protein Cdc42 plays an important role in mitotic exit following the spindle pole attachment . We show here that inhibition of Cdc42 activation blocks polar body formation. The oocytes initiate anaphase but fail to properly form and direct a contractile ring. Endogenous Cdc42 is activated at the spindle pole-cortical contact site immediately prior to polar body formation. The cortical Cdc42 activity zone, which directly overlays the spindle pole, is circumscribed by a cortical RhoA activity zone; the latter defines the cytokinetic contractile furrow . As the RhoA ring contracts during cytokinesis, the Cdc42 zone expands, maintaining its complementary relationship with the RhoA ring. Cdc42 signaling may thus be an evolutionarily conserved mechanism that couples spindle positioning to asymmetric cytokinesis.

We have combined genetic algorithm (GA) and all paired (AP) support vector machine (SVM) methods for multiclass cancer categorization. Predictive features can be automatically determined through iterative GA/SVM, leading to very compact sets of non-redundant cancer-relevant genes with the best classification performance reported to date. Interestingly, these different classifier sets harbor only modest overlapping gene features but have similar levels of accuracy in leave-one-out cross-validations (LOOCV). Further characterization of these optimal tumor discriminant features, including the use of nearest shrunken centroids (NSC), analysis of annotations and literature text mining, reveals previously unappreciated tumor subclasses and a series of genes that could be used as cancer biomarkers. With this approach, we believe that microarray-based multiclass molecular analysis can be an effective tool for cancer biomarker discovery and subsequent molecular cancer diagnosis.

Achieving good speed and accuracy trade-off on a target platform is very important in deploying deep neural networks in real world scenarios. However, most existing automatic architecture search approaches only concentrate on high performance. In this work, we propose an algorithm that can offer better speed/accuracy trade-off of searched networks, which is termed "Partial Order Pruning". It prunes the architecture search space with a partial order assumption to automatically search for the architectures with the best speed and accuracy trade-off. Our algorithm explicitly takes profile information about the inference speed on the target platform into consideration. With the proposed algorithm, we present several Dongfeng (DF) networks that provide high accuracy and fast inference speed on various application GPU platforms. By further searching decoder architectures, our DF-Seg real-time segmentation networks yield state-of-the-art speed/accuracy trade-off on both the target embedded device and the high-end GPU.

We report a novel strategy to construct temperature-responsive nanocarriers for controlled release based on mesoporous silica and reversible single-stranded DNA valves.