Proteasome-associated ubiquitin ligase relays target plant hormone-specific transcriptional activators

The ubiquitin-proteasome system is vital to hormone-mediated developmental and stress responses in plants. Ubiquitin ligases target hormone-specific transcriptional activators (TAs) for degradation, but how TAs are processed by proteasomes remains unknown. We report that in Arabidopsis the salicylic acid- and ethylene-responsive TAs, NPR1 and EIN3, are relayed from pathway-specific ubiquitin ligases to proteasome-associated HECT-type UPL3/4 ligases. Activity and stability of NPR1 was regulated by sequential action of three ubiquitin ligases, including UPL3/4, while proteasome processing of EIN3 required physical handover between ethylene-responsive SCFEBF2 and UPL3/4 ligases. Consequently, UPL3/4 controlled extensive hormone-induced developmental and stress-responsive transcriptional programmes. Thus, our findings identify unknown ubiquitin ligase relays that terminate with proteasome-associated HECT-type ligases, which may be a universal mechanism for processive degradation of proteasome-targeted TAs and other substrates.

SACBP: Belief space planning for continuous-time dynamical systems via stochastic sequential action control

We propose a novel belief space planning technique for continuous dynamics by viewing the belief system as a hybrid dynamical system with time-driven switching. Our approach is based on the perturbation theory of differential equations and extends sequential action control to stochastic dynamics. The resulting algorithm, which we name SACBP, does not require discretization of spaces or time and synthesizes control signals in near real-time. SACBP is an anytime algorithm that can handle general parametric Bayesian filters under certain assumptions. We demonstrate the effectiveness of our approach in an active sensing scenario and a model-based Bayesian reinforcement learning problem. In these challenging problems, we show that the algorithm significantly outperforms other existing solution techniques including approximate dynamic programming and local trajectory optimization.

PROTECTION WITH A BIOACTIVE COMPOSITION BASED ON PLUMBAGIN (CERATOSTIGMA PLUMBAGINOIDES BUNGE) AND NA-ASCORBATE PIGMENTS AND PSII IN OXIDATIVE STRESS

The protective properties of Na-ascorbate and a bioactive composition (BAC) obtained on the basis of plumbagin from roots (Ceratostigma plumbaginoides Bunge) under the toxic effect of Zn2+ and Ni2+ on wheat seedlings (Triticum aestivum L.) were studied. The change in the characteristics of millisecond delayed fluorescence (msec DF Chl a) reflecting the state of PS II shows a decrease in the blocking of the activity of the electron transport chain (ETC) on the acceptor side under the action of Zn2+ and Ni2+ and on the donor side under the action of Ni2+. The resistance of Chl b 650 decreases and the resistance of carotenoids to this stress increases. The action of Na-asс and BAC changes the ratio of pigments. Na-asc restores the activity of the donor side and increases the work of the acceptor side of the ETC upon sequential action with Zn2+. The activity of the acceptor side of the ETC is restored with the simultaneous action of Na-asc with Ni2+. The effect of the BAC is manifested in the restoration of the activity of the donor side of the ETC PS II with sequential action with metals. The corrective effect of Na-asc and LHC is determined by their ability to neutralize reactive oxygen species formed under stress and to support the redox reactions of photosystems.

The role of action effects in motor sequence planning and execution: exploring the influence of temporal and spatial effect anticipation

Actions we perform every day generate perceivable outcomes with both spatial and temporal features. According to the ideomotor principle, we plan our actions by anticipating the outcomes, but this principle does not directly address how sequential movements are influenced by different outcomes. We examined how sequential action planning is influenced by the anticipation of temporal and spatial features of action outcomes. We further explored the influence of action sequence switching. Participants performed cued sequences of button presses that generated visual effects which were either spatially compatible or incompatible with the sequences, and the spatial effects appeared after a short or long delay. The sequence cues switched or repeated across trials, and the predictability of action sequence switches was varied across groups. The results showed a delay-anticipation effect for sequential action, whereby a shorter anticipated delay between action sequences and their outcomes speeded initiation and execution of the cued action sequences. Delay anticipation was increased by predictable action switching, but it was not strongly modified by the spatial compatibility of the action outcomes. The results extend previous demonstrations of delay anticipation to the context of sequential action. The temporal delay between actions and their outcomes appears to be retrieved for sequential planning and influences both the initiation and the execution of actions.

Testing the Change, Improving the Plan

Chapter 6 introduces the students to a proprietary framework and tools to test, refine, and prepare each student’s system improvement plan (SIP) for implementation. The students revisit their country SIP to refine its constituent process steps and to produce a process flow map of their plan. The flow map illustrates a logical chain that links the proposed inputs through sequential action steps for implementation of the plan to produce the outputs that enable the SIP to attain the projected goals. The authors introduce the proprietary D3A3 model that consists of six interlinked steps: D1—design the intervention plan; D2—determine the measures of success; D3—deliver; A1—assess; A2—analyze; and A3—act. The D3A3 model is used to guide iterative development and agile implementation of system improvement plans. The students will use the D3A3 model to conduct one or more test runs to improve their SIP and increase their confidence that it will deliver the outputs they need. In complex dynamic systems, it is hard to predict the results of change and plans should be subjected to iterative cycles of testing. For this the authors introduce a process called a model for improvement that helps designers to understand the potential impact, effort, feasibility, and risks of implementing their SIP and to progressively test and strengthen plan design and prepare it for implementation.

Single-molecule insight into stalled replication fork rescue in Escherichia coli

DNA replication forks stall at least once per cell cycle in Escherichia coli. DNA replication must be restarted if the cell is to survive. Restart is a multi-step process requiring the sequential action of several proteins whose actions are dictated by the nature of the impediment to fork progression. When fork progress is impeded, the sequential actions of SSB, RecG and the RuvABC complex are required for rescue. In contrast, when a template discontinuity results in the forked DNA breaking apart, the actions of the RecBCD pathway enzymes are required to resurrect the fork so that replication can resume. In this review, we focus primarily on the significant insight gained from single-molecule studies of individual proteins, protein complexes, and also, partially reconstituted regression and RecBCD pathways. This insight is related to the bulk-phase biochemical data to provide a comprehensive review of each protein or protein complex as it relates to stalled DNA replication fork rescue.

The Sequential Action of MIDA9/PP2C.D1, PP2C.D2, and PP2C.D5 Is Necessary to Form and Maintain the Hook After Germination in the Dark

During seedling etiolation after germination in the dark, seedlings have closed cotyledons and form an apical hook to protect the meristem as they break through the soil to reach the surface. Once in contact with light, the hook opens and cotyledons are oriented upward and separate. Hook development in the dark after seedling emergence from the seed follows three distinctly timed and sequential phases: formation, maintenance, and eventual opening. We previously identified MISREGULATED IN DARK9 (MIDA9) as a phytochrome interacting factor (PIF)-repressed gene in the dark necessary for hook development during etiolated growth. MIDA9 encodes the type 2C phosphatase PP2C.D1, and pp2c-d1/mida9 mutants exhibit open hooks in the dark. Recent evidence has described that PP2C.D1 and other PP2C.D members negatively regulate SMALL AUXIN UP RNA (SAUR)-mediated cell elongation. However, the fundamental question of the timing of PP2C.D1 action (and possibly other members of the PP2C.D family) during hook development remains to be addressed. Here, we show that PP2C.D1 is required immediately after germination to form the hook. pp2c.d1/mida9 shows reduced cell expansion in the outer layer of the hook and, therefore, does not establish the differential cell growth necessary for hook formation, indicating that PP2C.D1 is necessary to promote cell elongation during this early stage. Additionally, genetic analyses of single and high order mutants in PP2C.D1, PP2C.D2, and PP2C.D5 demonstrate that the three PP2C.Ds act collectively and sequentially during etiolation: whereas PP2C.D1 dominates hook formation, PP2C.D2 is necessary during the maintenance phase, and PP2C.D5 acts to prevent opening during the third phase together with PP2C.D1 and PP2C.D2. Finally, we uncover a possible connection of PP2C.D1 levels with ethylene physiology, which could help optimize hook formation during post-germinative growth in the dark.

Reinforcement with Fading Memories

We study the effect of imperfect memory on decision making in the context of a stochastic sequential action-reward problem. An agent chooses a sequence of actions, which generate discrete rewards at different rates. She is allowed to make new choices at rate β, whereas past rewards disappear from her memory at rate μ. We focus on a family of decision rules where the agent makes a new choice by randomly selecting an action with a probability approximately proportional to the amount of past rewards associated with each action in her memory. We provide closed form formulas for the agent’s steady-state choice distribution in the regime where the memory span is large ([Formula: see text]) and show that the agent’s success critically depends on how quickly she updates her choices relative to the speed of memory decay. If [Formula: see text], the agent almost always chooses the best action (that is, the one with the highest reward rate). Conversely, if [Formula: see text], the agent chooses an action with a probability roughly proportional to its reward rate.