Calcineurin B-Like Proteins CBL4 and CBL10 Mediate Two Independent Salt Tolerance Pathways in Arabidopsis.

In Arabidopsis, the salt overly sensitive (SOS) pathway, consisting of calcineurin B-like protein 4 (CBL4/SOS3), CBL-interacting protein kinase 24 (CIPK24/SOS2) and SOS1, has been well defined as a crucial mechanism to control cellular ion homoeostasis by extruding Na+ to the extracellular space, thus conferring salt tolerance in plants. CBL10 also plays a critical role in salt tolerance possibly by the activation of Na+ compartmentation into the vacuole. However, the functional relationship of the SOS and CBL10-regulated processes remains unclear. Here, we analyzed the genetic interaction between CBL4 and CBL10 and found that the cbl4 cbl10 double mutant was dramatically more sensitive to salt as compared to the cbl4 and cbl10 single mutants, suggesting that CBL4 and CBL10 each directs a different salt-tolerance pathway. Furthermore, the cbl4 cbl10 and cipk24 cbl10 double mutants were more sensitive than the cipk24 single mutant, suggesting that CBL10 directs a process involving CIPK24 and other partners different from the SOS pathway. Although the cbl4 cbl10, cipk24 cbl10, and sos1 cbl10 double mutants showed comparable salt-sensitive phenotype to sos1 at the whole plant level, they all accumulated much lower Na+ as compared to sos1 under high salt conditions, suggesting that CBL10 regulates additional unknown transport processes that play distinct roles from the SOS1 in Na+ homeostasis

Evolution of fish schools: a theoretical study by agent-based simulation and game-theoretic analysis

This thesis aimed to identify the underlying mechanism which drives fish to evolve into the schooling behaviour under predation. Previous works used to explain this formation by group benefits, individual differences or evolutionary trade-offs. However, conflicts from these explanations to the natural fish are considerable. Based on agent-based simulations and game-theoretic analyses, this thesis demonstrated that the intraspecies competition in a relatively homogeneous population is sufficient to cause the evolution of fish schools. It has been shown that when predators exhibit the ‘marginal predation’, that is, only catching prey on the margin of a group, prey fish must evolve to form a selfish herd, which will be increasingly crowded until no outer fish can enter it. In this case, fish should evolve to leave the group margin together, as the originally reported ‘collective departure strategy’, to expose the inner fish and share the risk. This adaptation then leads to the emergence of fish schools. The thesis further showed that the ‘marginal predation’ of predators and the ‘collective departure’ of prey is able to form an evolutionarily stable state in a coevolutionary system, which completed the explanation of the evolution of fish schools in nature

Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs

Privacy-Preserving Quantum Two-Party Geometric Intersection

Privacy-preserving computational geometry is the research area on the intersection of the domains of secure multi-party computation (SMC) and computational geometry. As an important field, the privacy-preserving geometric intersection (PGI) problem is when each of the multiple parties has a private geometric graph and seeks to determine whether their graphs intersect or not without revealing their private information. In this study, through representing Alice's (Bob's) private geometric graph G_A (G_B) as the set of numbered grids S_A (S_B), an efficient privacy-preserving quantum two-party geometric intersection (PQGI) protocol is proposed. In the protocol, the oracle operation O_A (O_B) is firstly utilized to encode the private elements of S_A=(a_0, a_1, ..., a_(M-1)) (S_B=(b_0, b_1, ..., b_(N-1))) into the quantum states, and then the oracle operation O_f is applied to obtain a new quantum state which includes the XOR results between each element of S_A and S_B. Finally, the quantum counting is introduced to get the amount (t) of the states |a_i+b_j> equaling to |0>, and the intersection result can be obtained by judging t>0 or not. Compared with classical PGI protocols, our proposed protocol not only has higher security, but also holds lower communication complexity

The Improvement of Reliability of High-k/Metal Gate pMOSFET Device with Various PMA Conditions

The oxygen and nitrogen were shown to diffuse through the TiN layer in the high-k/metal gate devices during PMA. Both the oxygen and nitrogen annealing will reduce the gate leakage current without increasing oxide thickness. The threshold voltages of the devices changed with various PMA conditions. The reliability of the devices, especially for the oxygen annealed devices, was improved after PMA treatments

Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains

We propose a framework for learning a fragment of probabilistic computation tree logic (pCTL) formulae from a set of states that are labeled as safe or unsafe. We work in a relational setting and combine ideas from relational Markov Decision Processes with pCTL model-checking. More specifically, we assume that there is an unknown relational pCTL target formula that is satisfied by only safe states, and has a horizon of maximum $k$ steps and a threshold probability $\alpha$. The task then consists of learning this unknown formula from states that are labeled as safe or unsafe by a domain expert. We apply principles of relational learning to induce a pCTL formula that is satisfied by all safe states and none of the unsafe ones. This formula can then be used as a safety specification for this domain, so that the system can avoid getting into dangerous situations in future. Following relational learning principles, we introduce a candidate formula generation process, as well as a method for deciding which candidate formula is a satisfactory specification for the given labeled states. The cases where the expert knows and does not know the system policy are treated, however, much of the learning process is the same for both cases. We evaluate our approach on a synthetic relational domain.Comment: 25 pages, 3 figures, 5 tables, 2 algorithms, preprin

USE JD-R THEORY TO EXPLORE THE RELATIONSHIP BETWEEN EMPLOYEE EXPERIENCE AND EMPLOYEE ENGAGEMENT—TAKING JOB DEMANDS AS THE MODERATING VARIABLE

Past research has proven that employee experience has a positive impact on employee engagement. Based on the conceptual framework of Job Demands-Resources model (JD-R) model, this study regards efficient employee experience as a job resource to explore the impact of "employee experience" and” job demands” on employee engagement in organizations. Work requirements are further divided into challenge demand and hindrance demand. This study adopts the experimental design of the scenario method and uses two two-factor independent sample designs, namely 2x2(employee experience is high / employee experience is low x challenging job demands is high / challenging job demands is low) and 2x2(employee experience is high / employee experience is low x hindering job demands is high / hindering job demands is low).A total of 176 valid questionnaires were collected. The research results found that when employee experience is high, employee engagement is higher than when employee experience is low. Employee experience and job demands have an interactive effect on employee engagement. When employee experience is high, employee engagement will be higher when challenging job demands are added than when hindering job demands are added. It is expected that the results of this study can help in theoretical and practical application