SPA-GPT: General Pulse Tailor for Simple Power Analysis Based on Reinforcement Learning

Power analysis of public-key algorithms is a well-known approach in the community of side-channel analysis. We usually classify operations based on the differences in power traces produced by different basic operations (such as modular exponentiation) to recover secret information like private keys. The more accurate the segmentation of power traces, the higher the efficiency of their classification. There exist two commonly used methods: one is equidistant segmentation, which requires a fixed number of basic operations and similar trace lengths for each type of operation, leading to limited application scenarios; the other is peak-based segmentation, which relies on personal experience to configure parameters, resulting in insufficient flexibility and poor universality. In this paper, we propose an automated power trace segmentation method based on reinforcement learning algorithms, which is applicable to a wide range of common implementation of public-key algorithms. Reinforcement learning is an unsupervised machine learning technique that eliminates the need for manual label collection. For the first time, this technique is introduced into the field of side-channel analysis for power trace processing. By using prioritized experience replay optimized Deep Q-Network algorithm, we reduce the number of parameters required to achieve accurate segmentation of power traces to only one, i.e. the key length. We also employ various techniques to improve the segmentation effectiveness, such as clustering algorithm, enveloped-based feature enhancement and fine-tuning method. We validate the effectiveness of the new method in nine scenarios involving hardware and software implementations of different public-key algorithms executed on diverse platforms such as microcontrollers, SAKURA-G, and smart cards. Specifically, one of these implementations is protected by time randomization countermeasures. Experimental results show that our method has good robustness on the traces with varying segment lengths and differing peak heights. After employ the clustering algorithm, our method achieves an accuracy of over 99.6% in operations recovery. Besides, power traces collected from these devices have been uploaded as databases, which are available for researchers engaged in public-key algorithms to conduct related experiments or verify our method

Tunable inductive coupler for high fidelity gates between fluxonium qubits

The fluxonium qubit is a promising candidate for quantum computation due to its long coherence times and large anharmonicity. We present a tunable coupler that realizes strong inductive coupling between two heavy-fluxonium qubits, each with $\sim50$MHz frequencies and $\sim5$ GHz anharmonicities. The coupler enables the qubits to have a large tuning range of $\textit{XX}$ coupling strengths ($-35$ to $75$ MHz). The $\textit{ZZ}$ coupling strength is $<3$kHz across the entire coupler bias range, and $<100$Hz at the coupler off-position. These qualities lead to fast, high-fidelity single- and two-qubit gates. By driving at the difference frequency of the two qubits, we realize a $\sqrt{i\mathrm{SWAP}}$ gate in $258$ns with fidelity $99.72\%$, and by driving at the sum frequency of the two qubits, we achieve a $\sqrt{b\mathrm{SWAP}}$ gate in $102$ns with fidelity $99.91\%$. This latter gate is only 5 qubit Larmor periods in length. We run cross-entropy benchmarking for over $20$ consecutive hours and measure stable gate fidelities, with $\sqrt{b\mathrm{SWAP}}$ drift ($2 \sigma$) $< 0.02\%$ and $\sqrt{i\mathrm{SWAP}}$ drift $< 0.08\%$.Comment: 16 pages, 14 figure

Magnetic Manganese Oxide Sweetgum-Ball Nanospheres with Large Mesopores Regulate Tumor Microenvironments for Enhanced Tumor Nanotheranostics.

An important objective of cancer nanomedicine is to improve the delivery efficacy of functional agents to solid tumors for effective cancer imaging and therapy. Stimulus-responsive nanoplatforms can target and regulate the tumor microenvironment (TME) for the optimization of cancer theranostics. Here, we developed magnetic manganese oxide sweetgum-ball nanospheres (MMOSs) with large mesopores as tools for improved cancer theranostics. MMOSs contain magnetic iron oxide nanoparticles and mesoporous manganese oxide (MnO2) nanosheets, which are assembled into gumball-like structures on magnetic iron oxides. The large mesopores of MMOSs are suited for cargo loading with chlorin e6 (Ce6) and doxorubicin (DOX), thus producing so-called CD@MMOSs. The core of magnetic iron oxides could achieve magnetic targeting of tumors under a magnetic field (0.25 mT), and the targeted CD@MMOSs may decompose under TME conditions, thereby releasing loaded cargo molecules and reacting with endogenous hydrogen peroxide (H2O2) to generate oxygen (O2) and manganese (II) ions (Mn2+). Investigation in vivo in tumor-bearing mice models showed that the CD@MMOS nanoplatforms achieved TME-responsive cargo release, which might be applied in chemotherapy and photodynamic therapy. A remarkable in vivo synergy of diagnostic and therapeutic functionalities was achieved by the decomposition of CD@MMOSs and coadministration with chemo-photodynamic therapy of tumors using the magnetic targeting mechanism. Thus, the result of this study demonstrates the feasibility of smart nanotheranostics to achieve tumor-specific enhanced combination therapy

Risk factors and prediction model of sleep disturbance in patients with maintenance hemodialysis: A single center study

ObjectivesThis study aimed to explore the risk factors and develop a prediction model of sleep disturbance in maintenance hemodialysis (MHD) patients.MethodsIn this study, 193 MHD patients were enrolled and sleep quality was assessed by Pittsburgh Sleep Quality Index. Binary logistic regression analysis was used to explore the risk factors for sleep disturbance in MHD patients, including demographic, clinical and laboratory parameters, and that a prediction model was developed on the basis of risk factors by two-way stepwise regression. The final prediction model is displayed by nomogram and verified internally by bootstrap resampling procedure.ResultsThe prevalence of sleep disturbance and severe sleep disturbance in MHD patients was 63.73 and 26.42%, respectively. Independent risk factors for sleep disturbance in MHD patients included higher 0.1*age (OR = 1.476, 95% CI: 1.103–1.975, P = 0.009), lower albumin (OR = 0.863, 95% CI: 0.771–0.965, P = 0.010), and lower 10*calcium levels (OR = 0.747, 95% CI: 0.615–0.907, P = 0.003). In addition, higher 0.1*age, lower albumin levels, and anxiety were independently associated with severe sleep disturbance in MHD patients. A risk prediction model of sleep disturbance in MHD patients showed that the concordance index after calibration is 0.736, and the calibration curve is approximately distributed along the reference line.ConclusionsOlder age, lower albumin and calcium levels are higher risk factors of sleep disturbance in MHD, and the prediction model for the assessment of sleep disturbance in MHD patients has excellent discrimination and calibration

Low Level of Low-Density Lipoprotein Receptor-Related Protein 1 Predicts an Unfavorable Prognosis of Hepatocellular Carcinoma after Curative Resection

BACKGROUND: Low-density lipoprotein receptor-related protein 1 (LRP1) is a multifunctional receptor involved in receptor-mediated endocytosis and cell signaling. The aim of this study was to elucidate the expression and mechanism of LRP1 in hepatocellular carcinoma (HCC). METHODS: LRP1 expression in 4 HCC cell lines and 40 HCC samples was detected. After interruption of LRP1 expression in a HCC cell line either with specific lentiviral-mediated shRNA LRP1 or in the presence of the LRP1-specific chaperone, receptor-associated protein (RAP), the role of LRP1 in the migration and invasion of HCC cells was assessed in vivo and in vitro, and the expression of matrix metalloproteinase (MMP) 9 in cells and the bioactivity of MMP9 in the supernatant were assayed. The expression and prognostic value of LRP1 were investigated in 327 HCC specimens. RESULTS: Low LRP1 expression was associated with poor HCC prognosis, with low expression independently related to shortened overall survival and increased tumor recurrence rate. Expression of LRP1 in non-recurrent HCC samples was significantly higher than that in early recurrent samples. LRP1 expression in HCC cell lines was inversely correlated with their metastatic potential. After inhibition of LRP1, low-metastatic SMCC-7721 cells showed enhanced migration and invasion and increased expression and bioactivity of MMP9. Correlation analysis showed a negative correlation between LRP1 and MMP9 expression in HCC patients. The prognostic value of LRP1 expression was validated in the independent data set. CONCLUSIONS: LRP1 modulated the level of MMP9 and low level of LRP1 expression was associated with aggressiveness and invasiveness in HCCs. LRP1 offered a possible strategy for tumor molecular therapy

Suppression of MAPK11 or HIPK3 reduces mutant Huntingtin levels in Huntington's disease models.

Most neurodegenerative disorders are associated with accumulation of disease-relevant proteins. Among them, Huntington disease (HD) is of particular interest because of its monogenetic nature. HD is mainly caused by cytotoxicity of the defective protein encoded by the mutant Huntingtin gene (HTT). Thus, lowering mutant HTT protein (mHTT) levels would be a promising treatment strategy for HD. Here we report two kinases HIPK3 and MAPK11 as positive modulators of mHTT levels both in cells and in vivo. Both kinases regulate mHTT via their kinase activities, suggesting that inhibiting these kinases may have therapeutic values. Interestingly, their effects on HTT levels are mHTT-dependent, providing a feedback mechanism in which mHTT enhances its own level thus contributing to mHTT accumulation and disease progression. Importantly, knockout of MAPK11 significantly rescues disease-relevant behavioral phenotypes in a knockin HD mouse model. Collectively, our data reveal new therapeutic entry points for HD and target-discovery approaches for similar diseases