MEKK3 Regulates IFN-γ Production in T Cells through the Rac1/2-Dependent MAPK Cascades

MEKK3 is a conserved Ser/Thr protein kinase belonging to the MAPK kinase kinase (MAP3K) family. MEKK3 is constitutively expressed in T cells, but its function in T cell immunity has not been fully elucidated. Using Mekk3 T cell conditional knockout (T-cKO) mice, we show that MEKK3 is required for T cell immunity in vivo. Mekk3 T-cKO mice had reduced T cell response to bacterial infection and were defective in clearing bacterial infections. The Ag-induced cytokine production, especially IFN-γ production, was impaired in Mekk3-deficient CD4 T cells. The TCR-induced ERK1/2, JNK, and p38 MAPKs activation was also defective in Mekk3-deficient CD4 T cells. In vitro, MEKK3 is not required for Th1 and Th2 cell differentiation. Notably, under a nonpolarizing condition (Th0), Mekk3 deficiency led to a significant reduction of IFN-γ production in CD4 T cells. Furthermore, the IL-12/IL-18–driven IFN-γ production and MAPK activation in Mekk3-deficient T cells was not affected suggesting that MEKK3 may selectively mediate the TCR-induced MAPK signals for IFN-γ production. Finally, we found that MEKK3 activation by TCR stimulation requires Rac1/2. Taken together, our study reveals a specific role of MEKK3 in mediating the TCR signals for IFN-γ production. Mitogen-activated protein kinases, including the ERK, JNK, and p38 MAPKs, are key intracellular signaling molecules used by eukaryotic cells to transduce a wide spectrum of extracellular signals (1–3). MAPKs play important roles in immune responses and regulate immune cell development, activation, differentiation, and survival. MAPK is activated through a cascade that also includes a MAPK kinase (MAP2K) and a MAP2K kinase (MAP3K). Upon stimulation of immune receptors or proinflammatory cytokine receptors, the MAPK pathways are rapidly induced, leading to the expression of genes that are essential for both the innate and adaptive immune responses (4–7). However, precisely how MAPKs are specifically induced and the physiological roles of individual MAPK cascades in T cell immunity remain to be fully elucidated. MEKK3 is a member of the MAP3K superfamily and shares substantial homology in the kinase domain with other members in this family (8–10). Biochemical studies showed that MEKK3 is able to activate multiple MAPKs including the JNKs, ERK1/2, p38, and ERK5 MAPKs in vitro under certain conditions (11, 12). Mouse gene knockout studies showed that MEKK3 is essential for embryonic cardiovascular development, a function that cannot be compensated by its closest homologue MEKK2 (10, 13). Activation of MEKK3, like MEKK2, requires a dimerization-induced autophosphorylation on a conserved serine residue in its activation loop (14, 15). MEKK3 is involved in the proinflammatory cytokine- and TLR-induced JNK and p38 MAPK activation and is also required for IKK–NF-κB activation (16–19). Several studies, including two recent reports, suggest that MEKK3 may cooperate with another MAP3K, TAK1, and act downstream of E3 ubiquitin ligase TRAF6 in mediating the proinflammatory signals for MAPK and IKK–NF-κB activation (19–22). MEKK3 is constitutively expressed in both innate and adaptive immune cells, but little is known about the role of MEKK3 in adaptive immune responses. Using a Mekk3 T cell conditional knockout (T-cKO) mouse line generated recently in our laboratory, we reported that MEKK3 is dispensable for thymic T cell development on the C57BL/6 background. However, the peripheral T cell homeostasis is impaired in the Mekk3 T-cKO mice (23). Similar phenotype in peripheral T cells homeostasis was also reported by another group with independently generated Mekk3 T-cKO mice (24). To understand further the physiological role of MEKK3 in T cell-mediated adaptive immunity, we studied the function of Mekk3-deficient T cells and examined how MEKK3 deficiency affects the Ag-induced cytokine production, anti-bacteria immunity, and signal transduction pathways. Our results demonstrate that MEKK3 is required for mounting optimal T cell responses in vivo and is involved in mediating the TCR-dependent Rac1/2 signals for IFN-γ production through the MAPK pathways

Design and resource management of reconfigurable multiprocessors for data-parallel applications

FPGA (Field-Programmable Gate Array)-based custom reconfigurable computing machines have established themselves as low-cost and low-risk alternatives to ASIC (Application-Specific Integrated Circuit) implementations and general-purpose microprocessors in accelerating a wide range of computation-intensive applications. Most often they are Application Specific Programmable Circuiits (ASPCs), which are developer programmable instead of user programmable. The major disadvantages of ASPCs are minimal programmability, and significant time and energy overheads caused by required hardware reconfiguration when the problem size outnumbers the available reconfigurable resources; these problems are expected to become more serious with increases in the FPGA chip size. On the other hand, dominant high-performance computing systems, such as PC clusters and SMPs (Symmetric Multiprocessors), suffer from high communication latencies and/or scalability problems. This research introduces low-cost, user-programmable and reconfigurable MultiProcessor-on-a-Programmable-Chip (MPoPC) systems for high-performance, low-cost computing. It also proposes a relevant resource management framework that deals with performance, power consumption and energy issues. These semi-customized systems reduce significantly runtime device reconfiguration by employing userprogrammable processing elements that are reusable for different tasks in large, complex applications. For the sake of illustration, two different types of MPoPCs with hardware FPUs (floating-point units) are designed and implemented for credible performance evaluation and modeling: the coarse-grain MIMD (Multiple-Instruction, Multiple-Data) CG-MPoPC machine based on a processor IP (Intellectual Property) core and the mixed-mode (MIMD, SIMD or M-SIMD) variant-grain HERA (HEterogeneous Reconfigurable Architecture) machine. In addition to alleviating the above difficulties, MPoPCs can offer several performance and energy advantages to our data-parallel applications when compared to ASPCs; they are simpler and more scalable, and have less verification time and cost. Various common computation-intensive benchmark algorithms, such as matrix-matrix multiplication (MMM) and LU factorization, are studied and their parallel solutions are shown for the two MPoPCs. The performance is evaluated with large sparse real-world matrices primarily from power engineering. We expect even further performance gains on MPoPCs in the near future by employing ever improving FPGAs. The innovative nature of this work has the potential to guide research in this arising field of high-performance, low-cost reconfigurable computing. The largest advantage of reconfigurable logic lies in its large degree of hardware customization and reconfiguration which allows reusing the resources to match the computation and communication needs of applications. Therefore, a major effort in the presented design methodology for mixed-mode MPoPCs, like HERA, is devoted to effective resource management. A two-phase approach is applied. A mixed-mode weighted Task Flow Graph (w-TFG) is first constructed for any given application, where tasks are classified according to their most appropriate computing mode (e.g., SIMD or MIMD). At compile time, an architecture is customized and synthesized for the TFG using an Integer Linear Programming (ILP) formulation and a parameterized hardware component library. Various run-time scheduling schemes with different performanceenergy objectives are proposed. A system-level energy model for HERA, which is based on low-level implementation data and run-time statistics, is proposed to guide performance-energy trade-off decisions. A parallel power flow analysis technique based on Newton\u27s method is proposed and employed to verify the methodology

Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success

Examining the Key Variables in Foreign Language Learning—A Case Study

Learning variables such as motivation, aptitude, attitude, learning strategies, personality and learning environment are deemed by English Language Teaching (ELT) researchers as crucial in contributing to a learner’s language achievement. This study focuses on the interrelated nature of these variables and examines how they affect language learners’ learning outcomes. The main purpose of this study was to find out about the complex network of these variables as well as the individual differences among learners. The data for this study were collected through interviews. The participants were seven Chinese English as a Foreign Language (EFL) learners who were residing in Canada.The study has yielded a number of interesting findings. One notable finding is that there seems to be some intricate interrelationships between learner characteristics (e.g., perseverance, attitude, motivation) and learner achievement. It appears that the role that learner perseverance and inner drive to learn play in their learning outcomes is as equally important as their learning environment and aptitude. Another conspicuous finding is that a correlation seems to exist between aptitude, motivation and success. Specifically, the higher aptitude learners exhibit, the more highly motivated they become and in turn, the more success they are likely to attain.The pedagogical implications from the study lies in the need to inform EFL teachers of the intricate interrelationships of the learning variables to help them better understand the complexities underlying the language learning process and enhance teacher training in how to make their teaching more truly communicative in nature

A Review of Studies on Teachers’ Roles and Their Limitations in PBL Presentation Context

At all levels of learning and teaching, it is known that teachers play important roles. A teacher’s role may be determined and studied from the functions he performs in different activities. These roles are often stereotyped but under the problem-based learning mode, they are flexible and deserve new studies since the transformation of the teaching and learning model entails a fundamental change of teacher role. As numerous research has been done on teachers’ roles, this paper intends to carry out a literature review of these studies and sort out the possible limitations in problem-based learning environment

Personalized Video Recommendation Using Rich Contents from Videos

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods