Transesterification Induced Multifunctional Additives Enable High-Performance Lithium Metal Batteries.

The electrolyte chemistry is crucially important for promoting the practical application of lithium metal batteries (LMBs). Here, we demonstrate for the first time that 1,3-dimethylimidazolium dimethyl phosphate (DIDP) and trimethylsilyl trifluoroacetate (TMSF) can undergo in situ transesterification in carbonate electrolyte to generate dimethyl trimethylsilyl phosphate (DTMSP) and 1,3-dimethylimidazolium trifluoroacetate (DITFA) as multifunctional additives for LMBs. H2O and HF can be removed by the Si-O group in DTMSP to improve the moisture resistance of electrolyte and the stability of cathode. Furthermore, the dissolution of lithium nitrate (LiNO3) in carbonate electrolyte can be promoted by the trifluoroacetate anion (TFA-) in DITFA, thereby optimizing the solvation structure and transport kinetics of Li+. More importantly, both DTMSP and DITFA tend to preferential redox decomposition due to the low lowest unoccupied molecular orbital (LUMO) and high highest occupied molecular orbital (HOMO). Consequently, a thin and robust layer rich in P/N/Si on the cathode and an inorganic-rich layer (e.g. Li3N/Li3P) on the anode can be constructed and superior electrochemical performances are achieved. This artificial transesterification strategy to introduce favorable additives paves an efficient and ingenious route to high-performance electrolyte for LMBs.

Case Report: Intervention of radiotherapy improves the prognosis of rectal squamous cell carcinoma with high PD-L1 expression and enable patients to obtain NED status.

Background: Rectal squamous cell carcinoma (RSCC) is a rare malignancy of the rectal tumor. Due to its extremely low incidence, there is still a lack of high-level treatment evidence and clinical consensus on this disease. Case report: In this article, we report a treatment process of RSCC with high PD-L1 expression. Firstly, this patient received 2 cycles of Pembrolizumab immunotherapy, but the efficacy was less sanguine. Subsequently, 4 cycles of mFOLFOX6 chemotherapy were synchronously performed on the basis of the initial regimen. Although partial remission was achieved in the lymph nodes thereafter, the changes in the primary lesions were still not significant. After that, the patient received radiotherapy, and followed by 6 cycles of PC (Albumin-binding Paclitaxel and Nedaplatin) regimen chemotherapy combined with Pembrolizumab. Eventually, the patient achieved no evidence of disease (NED) status, and no signs of recurrence or metastasis were found after 12 months of follow-up. Conclusion: This is the first report of a RSCC patient with high PD-L1 expression achieving a complete response. Looking back over the whole treatment process of this patient, we found that the participation of radiotherapy was the inflection point of prominent efficacy, which may provide a new idea for the selection of comprehensive treatment strategies for patients with RSCC.

CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning.

Removing noise from acquired images is a crucial step in various image processing and computer vision tasks. However, the existing methods primarily focus on removing specific noise and ignore the ability to work across modalities, resulting in limited generalization performance. Inspired by the iterative procedure of image processing used by professionals, we propose a pixel-wise crossmodal image-denoising method based on deep reinforcement learning to effectively handle noise across modalities. We proposed a similarity reward to help teach an optimal action sequence to model the step-wise nature of the human processing process explicitly. In addition, We designed an action set capable of handling multiple types of noise to construct the action space, thereby achieving successful crossmodal denoising. Extensive experiments against state-of-the-art methods on publicly available RGB, infrared, and terahertz datasets demonstrate the superiority of our method in crossmodal image denoising.

Altered nonlinear Granger causality interactions in the large-scale brain networks of patients with schizophrenia.

Objective.It has been demonstrated that schizophrenia (SZ) is characterized by functional dysconnectivity involving extensive brain networks. However, the majority of previous studies utilizing resting-state functional magnetic resonance imaging (fMRI) to infer abnormal functional connectivity (FC) in patients with SZ have focused on the linear correlation that one brain region may influence another, ignoring the inherently nonlinear properties of fMRI signals.Approach. In this paper, we present a neural Granger causality (NGC) technique for examining the changes in SZ's nonlinear causal couplings. We develop static and dynamic NGC-based analyses of large-scale brain networks at several network levels, estimating complicated temporal and causal relationships in SZ patients.Main results. We find that the NGC-based FC matrices can detect large and significant differences between the SZ and healthy control groups at both the regional and subnetwork scales. These differences are persistent and significantly overlapped at various network sparsities regardless of whether the brain networks were built using static or dynamic techniques. In addition, compared to controls, patients with SZ exhibited extensive NGC confusion patterns throughout the entire brain.Significance. These findings imply that the NGC-based FCs may be a useful method for quantifying the abnormalities in the causal influences of patients with SZ, hence shedding fresh light on the pathophysiology of this disorder.