Poly (ADP-ribose) polymerase (PARP) inhibitors offer a significant clinical benefit for triple-negative breast cancers (TNBCs) with BRCA1/2 mutation. However, the narrow clinical indication limits the development of PARP inhibitors. Phosphoinositide 3-kinase (PI3K) inhibition sensitizes BRCA-proficient TNBC to PARP inhibition, which broadens the indication of PARP inhibitors. Previously researches have reported that PI3K inhibition induced the defect of homologous recombination (HR) mediated repair by downregulating the expression of BRCA1/2 and Rad51. However, the mechanism for their synergistic effects in the treatment of TNBC is still unclear. Herein, we focused on DNA damage, DNA single-strand breaks (SSBs) repair and DNA double-strand breaks (DSBs) repair three aspects to investigate the mechanism of dual PI3K and PARP inhibition in DNA damage response. We found that dual PI3K and PARP inhibition with BKM120 and olaparib significantly reduced the proliferation of BRCA-proficient TNBC cell lines MDA-MB-231 and MDA231-LM2. BKM120 increased cellular ROS to cause DNA oxidative damage. Olaparib resulted in concomitant gain of PARP1, forkhead box M1 (FOXM1) and Exonuclease 1 (Exo1) while inhibited the activity of PARP. BKM120 downregulated the expression of PARP1 and PARP2 to assist olaparib in blocking PARP mediated repair of DNA SSBs. Meanwhile, BKM120 inhibited the expression of BRAC1/2 and Rad51/52 to block HR mediated repair through the PI3K/Akt/NFκB/c-Myc signaling pathway and PI3K/Akt/ FOXM1/Exo1 signaling pathway. BKM120 induced HR deficiency expanded the application of olaparib to HR proficient TNBCs. Our findings proved that PI3K inhibition impaired the repair of both DNA SSBs and DNA DSBs. FOXM1 and Exo1 are novel therapeutic targets that serves important roles in DNA damage response.

Unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been widely used in delivery. In the context of the COVID-2019, in order to control the development of the epidemic, many places have adopted measures to isolate and close the area once a confirmed case is found. While reducing the contact between people, it also blocks the normal driving of vehicles. Only by changing the traditional logistics and distribution methods can customers who have been in a closed and isolated area for a long time be served. Therefore, we use the Cooperative UGV-UAV to achieve it. In this paper, when commanding cooperative UGV and UAV for emergency resource delivery, we mainly focus on two questions: how to accept the operation order (OPORD) from the commander, how to generate a nested vehicle routing planning. We first employ one intelligent task understanding module to drive the intelligent unmanned vehicles to accept and process the C-BML (Coalition Battle Management Language) formatted OPORD with 5W (what, who, where, why, when) elements. Then, we slove the nested vehicle routing planning problem as a mixed integer linear program (MILP) with the outputs of what is the UGV route, what is the UGV sortie, and how to control the customers' distribution between the UGV and the UAV. Experimental results of random instances and case study show that using the iterative improvement algorithm increase the speed rate of solving more than 10%.

Hexagonal grids use a hierarchical subdivision tessellation to cover the entire plane or sphere. Due to the 6-fold rotational symmetry, hexagonal grids have some advantages (e.g. isoperimetry, equidistant neighbors, and uniform connectivity) over quadrangular and triangular girds, which makes them suitable to tackle tasks of geospatial information processing and intelligent decision-making. In this paper, we first introduce some applications based on the hexagonal grids. Then, we introduce the planer and spherical hexagonal grids and analyze the group representations for them, we review geometric deep learning, some Convolutional Neural Networks (CNNs) for hexagonal grids, and group-based equivariant convolution. Next in importance, we propose the HexagonNet for hexagonal grids, and define a new convolution operator and pooling operator. Finally, in order to evaluate the effectiveness of the proposed HexagonNet, we perform experiments on two tasks: aerial scene classification on the aerial image dataset (AID), and 3D shape classification on the ModelNet40 dataset. The experimental results verify the practical applicability of the HexagonNet given some fixed parameter budgets.

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This paper investigates cooperative trajectory planning of multiple unmanned combat aerial vehicles (multi-UCAV) in performing autonomous cooperative air-to-ground target attack missions. By integrating an approximate allowable attack region model, several constraint models, and a multicriterion objective function, the problem is formulated as a cooperative trajectory optimal control problem (CTOCP). Then, a virtual motion camouflage (VMC) for cooperative trajectory planning of multi-UCAV, combining with the differential flatness theory, Gauss pseudospectral method (GPM), and nonlinear programming, is designed to solve the CTOCP. In particular, the notion of the virtual time is introduced to the VMC problem formulation to handle the temporal cooperative constraints. The simulation experiments validate that the CTOCP can be effectively solved by the cooperative trajectory planning algorithm based on VMC which integrates the spatial and temporal constraints on the trajectory level, and the comparative experiments illustrate that VMC based algorithm is more efficient than GPM based direct collocation method in tackling the CTOCP.