Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder

In this paper, we present a hierarchical path planning framework called SG-RL (subgoal graphs-reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By "rational", we mean (1) efficient path planning to eliminate first-move lags; (2) collision-free and smooth for agents with kinematic constraints satisfied. SG-RL works in a two-level manner. At the first level, SG-RL uses a geometric path-planning method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract paths, also called subgoal sequences. At the second level, SG-RL uses an RL method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments. The second advantage is that, when the environment changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI can deal with uncertainties by exploiting its generalization ability to handle changes in environments. Simulation experiments in representative scenarios demonstrate that, compared with existing methods, SG-RL can work well on large-scale maps with relatively low action-switching frequencies and shorter path lengths, and SG-RL can deal with small changes in environments. We further demonstrate that the design of reward functions and the types of training environments are important factors for learning feasible policies.Comment: 20 page

Anaesthetics affect cancer cell biology through cellular signalling and metabolic modulations

Surgery remains the first-line treatment for most cancer types. However, anaesthetic use during surgery may influence cancer cell biology and hence affect cancer recurrence. The current PhD study project objective is to evaluate the effects of two general anaesthetics, sevoflurane (inhalational) and propofol (intravenous), on cancer cells. This study intends to understand the impact of anaesthetics on cancer recurrence following surgery and ultimately provide the research rationale of clinical study/trials for improving cancer surgical outcomes. Propofol inhibited lung, colonic, renal, and ovarian cancer cell malignancy in a dose- dependent manner through regulating PEDF* and HIF-1α expressions. Propofol also altered the metabolites of lung, colonic, renal, and ovarian cancer cells that inhibited the lung and ovarian cancer cell glucose metabolism. It was demonstrated that propofol inhibited lung cancer cell malignancy through downregulating GLUT1 and MPC1, thus disturbing glucose metabolism. These processes induced the high PEDF expression, which downregulated HIF-1α via the Akt pathways, thus regulated pro- and anti-tumour genes. However, propofol neither inhibited brain cancer cell malignancy nor affected the above molecular entities. In contrast, sevoflurane enhanced colonic, renal and ovarian cancer cell malignancy through upregulating VEGFA. Sevoflurane upregulated GLUT1, MPC1 and GLUD1, which enhanced ovarian cancer cellular glucose metabolism. These changes inhibited PEDF expression, and its reduction resulted in upregulating HIF-1α via the Erk pathway, which upregulated two pro-tumour gene-encoded proteins, namely CXCL12 and CXCR4. However, propofol had the opposite effect on these cellular signalling and metabolic pathways in ovarian cancer cells. In summary, this PhD project demonstrated that sevoflurane may have pro-tumour while propofol might have anti-cancer properties. According to laboratory evidence found in the current study, propofol, unlike sevoflurane, may benefit cancer surgery patients. The work presented in this thesis may provide a foundation for further clinical studies to optimise the anaesthesia regimen for better surgical outcomes following cancer surgery.Open Acces

Bis(1-methyl­piperazine-1,4-diium) tetra­chloridocuprate(II)

The title compound, (C5H14N2)[CuCl4], was synthesized by hydro­thermal reaction of CuCl2 with 1-methyl­piperazine in an HCl/water solution. Both amine N atoms are protonated. The piperazine ring adopts a chair conformation. The Cu—Cl distances in the tetrahedral anion are in the range 2.2360 (7)–2.2732 (7) Å. In the crystal, moderately strong and weak inter­molecular N—H⋯Cl hydrogen bonds link the anion and cation units into an infinite two-dimensional network parallel to the ab plane