UBE2A/B is the trans-acting factor mediating mechanotransduction and contact inhibition.

Mechanotransduction and contact inhibition (CI) control gene expression to regulate proliferation, differentiation, and even tumorigenesis of cells. However, their downstream trans-acting factors (TAFs) are not well known due to a lack of a high-throughput method to quantitatively detect them. Here, we developed a method to identify TAFs on the cis-acting sequences that reside in open chromatin or DNaseI-hypersensitive sites (DHSs) and to detect nucleocytoplasmic shuttling TAFs using computational and experimental screening. The DHS-proteomics revealed over 1000 potential mechanosensing TAFs and UBE2A/B (Ubiquitin-conjugating enzyme E2 A) was experimentally identified as a force- and CI-dependent nucleocytoplasmic shuttling TAF. We found that translocation of YAP/TAZ and UBE2A/B are distinctively regulated by inhibition of myosin contraction, actin-polymerization, and CI depending on cell types. Next-generation sequence analysis revealed many downstream genes including YAP are transcriptionally regulated by ubiquitination of histone by UBE2A/B. Our results suggested a YAP-independent mechanotransduction and CI pathway mediated by UBE2A/B.

A Task Scheduling Optimization Method for Vehicles Serving as Obstacles in Mobile Edge Computing Based IoV Systems.

In recent years, as more and more vehicles request service from roadside units (RSU), the vehicle-to-infrastructure (V2I) communication links are under tremendous pressure. This paper first proposes a dynamic dense traffic flow model under the condition of fading channel. Based on this, the reliability is redefined according to the real-time location information of vehicles. The on-board units (OBU) migrate intensive computing tasks to the appropriate RSU to optimize the execution time and calculating cost at the same time. In addition, competitive delay is introduced into the model of execution time, which can describe the channel resource contention and data conflict in dynamic scenes of the internet of vehicles (IoV). Next, the task scheduling for RSU is formulated as a multi-objective optimization problem. In order to solve the problem, a task scheduling algorithm based on a reliability constraint (TSARC) is proposed to select the optimal RSU for task transmission. When compared with the genetic algorithm (GA), there are some improvements of TSARC: first, the quick non-dominated sorting is applied to layer the population and reduce the complexity. Second, the elite strategy is introduced with an excellent nonlinear optimization ability, which ensures the diversity of optimal individuals and provides different preference choices for passengers. Third, the reference point mechanism is introduced to reserve the individuals that are non-dominated and close to reference points. TSARC's Pareto based multi-objective optimization can comprehensively measure the overall state of the system and flexibly schedule system resources. Furthermore, it overcomes the defects of the GA method, such as the determination of the linear weight value, the non-uniformity of dimensions among objectives, and poor robustness. Finally, numerical simulation results based on the British Highway Traffic Flow Data Set show that the TSARC performs better scalability and efficiency than other methods with different numbers of tasks and traffic flow densities, which verifies the previous theoretical derivation.