Uncertainty Modulates the Effect of Transcranial Stimulation Over the Right Dorsolateral Prefrontal Cortex on Decision-Making Under Threat

Threat is a strategy that can be used to impact decision-making processes in bargaining. Abundant evidence suggests that credible threat and incredible threat both influence the obeisance of others. However, it is not clear whether the decision-making processes under credible threat and incredible threat during bargaining involve differential neurocognitive mechanisms. Here, we employed cathodal transcranial direct current stimulation (tDCS) to deactivate the right dorsolateral prefrontal cortex (rDLPFC) to address this question while subjects allocated and reported the subjective probability of future rejection under incredible threat and credible threat. We found that application of cathodal tDCS over the rDLPFC decreased the proposer’s subjective inference of probability of rejection and the offer to the responder under incredible threat. Conversely, the same stimulation did not lead to a significant difference compared to the sham group in subjective probability and offer under credible threat. These results suggested that decision-making processes under the two types of threat during bargaining were associated with different neurocognitive substrates, because the punishment for non-compliance was uncertain under incredible threat, whereas it was certain under credible threat. We decreased activity in the rDLPFC, which is involved in decision-making processes related to bargaining under incredible threats, and observed significantly impacted behavior. The differential neurocognitive bases of subjective probability of rejection under incredible threat and credible threat resulted in different tDCS effects

Neural Dynamics of Processing Probability Weight and Monetary Magnitude in the Evaluation of a Risky Reward

Risky decision-making involves risky reward valuation, choice, and feedback processes. However, the temporal dynamics of risky reward processing are not well understood. Using event-related brain potential, we investigated the neural correlates of probability weight and money magnitude in the evaluation of a risky reward. In this study, each risky choice consisted of two risky options, which were presented serially to separate decision-making and option evaluation processes. The early P200 component reflected the process of probability weight, not money magnitude. The medial frontal negativity (MFN) reflected both probability weight and money magnitude processes. The late positive potential (LPP) only reflected the process of probability weight. These results demonstrate distinct temporal dynamics for probability weight and money magnitude processes when evaluating a risky outcome, providing a better understanding of the possible mechanism underlying risky reward processing

Transcranial Direct Current Stimulation of the Right Lateral Prefrontal Cortex Changes a priori Normative Beliefs in Voluntary Cooperation

A priori normative beliefs, the precondition of social norm compliance that reflects culture and values, are considered unique to human social behavior. Previous studies related to the ultimatum game revealed that right lateral prefrontal cortex (rLPFC) has no stimulation effects on normative beliefs. However, no research has focused on the effects of a priori belief on the rLPFC in voluntary cooperation attached to the public good (PG) game. In this study, we used a linear asymmetric PG to confirm the influence of the rLPFC on a priori normative beliefs without threats of external punishment through transcranial direct current stimulation (tDCS). Participants engaged via computer terminals in groups of four (i.e., two high-endowment players with 35G$and two low-endowment players with 23G$). They were anonymous and had no communication during the entire process. They were randomly assigned to receive 15 min of either anodal, cathodal, or sham stimulation and then asked to answer questions concerning a priori normative beliefs (norm.belief and pg.belief). Results suggested that anodal/cathodal tDCS significantly (P < 0.001) shifted the participants’ a priori normative beliefs in opposite directions compared to the shift in the sham group. In addition, different identities exhibited varying degrees of change (28.80–54.43%). These outcomes provide neural evidence of the rLPFC mechanism’s effect on the normative beliefs in voluntary cooperation based on the PG framework

CR-SFP: Learning Consistent Representation for Soft Filter Pruning

Soft filter pruning~(SFP) has emerged as an effective pruning technique for allowing pruned filters to update and the opportunity for them to regrow to the network. However, this pruning strategy applies training and pruning in an alternative manner, which inevitably causes inconsistent representations between the reconstructed network~(R-NN) at the training and the pruned network~(P-NN) at the inference, resulting in performance degradation. In this paper, we propose to mitigate this gap by learning consistent representation for soft filter pruning, dubbed as CR-SFP. Specifically, for each training step, CR-SFP optimizes the R-NN and P-NN simultaneously with different distorted versions of the same training data, while forcing them to be consistent by minimizing their posterior distribution via the bidirectional KL-divergence loss. Meanwhile, the R-NN and P-NN share backbone parameters thus only additional classifier parameters are introduced. After training, we can export the P-NN for inference. CR-SFP is a simple yet effective training framework to improve the accuracy of P-NN without introducing any additional inference cost. It can also be combined with a variety of pruning criteria and loss functions. Extensive experiments demonstrate our CR-SFP achieves consistent improvements across various CNN architectures. Notably, on ImageNet, our CR-SFP reduces more than 41.8\% FLOPs on ResNet18 with 69.2\% top-1 accuracy, improving SFP by 2.1\% under the same training settings. The code will be publicly available on GitHub.Comment: 11 pages, 4 figure

PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation

Reliable LiDAR panoptic segmentation (LPS), including both semantic and instance segmentation, is vital for many robotic applications, such as autonomous driving. This work proposes a new LPS framework named PANet to eliminate the dependency on the offset branch and improve the performance on large objects, which are always over-segmented by clustering algorithms. Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with the ``sampling-shifting-grouping" scheme to directly group thing points into instances from the raw point cloud efficiently. More specifically, balanced point sampling is introduced to generate sparse seed points with more uniform point distribution over the distance range. And a shift module, termed bubble shifting, is proposed to shrink the seed points to the clustered centers. Then we utilize the connected component label algorithm to generate instance proposals. Furthermore, an instance aggregation module is devised to integrate potentially fragmented instances, improving the performance of the SIP module on large objects. Extensive experiments show that PANet achieves state-of-the-art performance among published works on the SemanticKITII validation and nuScenes validation for the panoptic segmentation task.Comment: 8 pages, 3 figures, IROS202

An Innovative Approach for Gob-Side Entry Retaining With Thick and Hard Roof: A Case Study

An innovative roadway layout in a Chinese colliery based on gob-side entry retaining (GER) with thick and hard roof (THR) was introduced. Suspended roof is left with a large area in GER with THR, which leads to large area roof weighting (LARW). LARW for GER with THR and mechanism of shallow-hole blasting to force roof caving in GER were expounded. Key parameters of shallow-hole blasting to force roof caving are proposed. LS-DYNA3D was used to validate the rationality of those key parameters, and UDEC was used to discuss and validate shallow-hole blasting to force roof-caving effect by contrast to the model without blasting and the model with shallow-hole blasting. Moreover, shallow-hole blasting technology to force roof caving for GER with THR was carried out in the Chinese colliery as a case study. Field test indicates that shallow-hole blasting technology effectively controls ground deformation of GER with THR and prevents LARW

Functional characterization of D9, a novel deazaneplanocin a (DZNep) analog, in targeting acute myeloid leukemia (AML)

10.1371/journal.pone.0122983PLoS ONE104e012298

Engineering the First Chimeric Antibody in Targeting Intracellular PRL-3 Oncoprotein for Cancer Therapy in Mice

Antibodies are considered as ‘magic bullets’ because of their high specificity. It is believed that antibodies are too large to routinely enter the cytosol, thus antibody therapeutic approach has been limited to extracellular or secreted proteins expressed by cancer cells. However, many oncogenic proteins are localized within the cell. To explore the possibility of antibody therapies against intracellular targets, we generated a chimeric antibody targeting the intracellular PRL-3 oncoprotein to assess its antitumor activities in mice. Remarkably, we observed that the PRL-3 chimeric antibody could efficiently and specifically reduce the formation of PRL-3 expressing metastatic tumors. We further found that natural killer (NK) cells were important in mediating the therapeutic effect, which was only observed in a nude mouse model (T-cell deficient), but not in a Severe Combined Immunodeficiency’ (scid) mouse model (B- and T-cell deficient), indicating the anticancer effect also depends on host B-cell activity. Our study involving 377 nude and scid mice suggests that antibodies targeting intracellular proteins can be developed to treat cancer