Boosting Adversarial Transferability by Block Shuffle and Rotation

Adversarial examples mislead deep neural networks with imperceptible perturbations and have brought significant threats to deep learning. An important aspect is their transferability, which refers to their ability to deceive other models, thus enabling attacks in the black-box setting. Though various methods have been proposed to boost transferability, the performance still falls short compared with white-box attacks. In this work, we observe that existing input transformation based attacks, one of the mainstream transfer-based attacks, result in different attention heatmaps on various models, which might limit the transferability. We also find that breaking the intrinsic relation of the image can disrupt the attention heatmap of the original image. Based on this finding, we propose a novel input transformation based attack called block shuffle and rotation (BSR). Specifically, BSR splits the input image into several blocks, then randomly shuffles and rotates these blocks to construct a set of new images for gradient calculation. Empirical evaluations on the ImageNet dataset demonstrate that BSR could achieve significantly better transferability than the existing input transformation based methods under single-model and ensemble-model settings. Combining BSR with the current input transformation method can further improve the transferability, which significantly outperforms the state-of-the-art methods

Leukocyte transcriptome of Cushing’s disease are associated with nerve impairment and psychiatric disorders

Introduction: The hypothalamus-pituitary-adrenal (HPA) axis and its end product cortisol is a major response mechanism to stress and plays a critical role in many psychiatric disorders. Cushing’s disease (CD) serves as a valuable in vivo “hyperexpression” model to elucidate the effect of cortisol on brain function and mental disorders. Changes in brain macroscale properties measured by magnetic resonance imaging (MRI) have been detailed demonstrated, but the biological and molecular mechanisms underlying these changes remain poorly understood. Material and methods: Here we included 25 CD patients and matched 18 healthy controls for assessment, and performed transcriptome sequencing of peripheral blood leukocytes. Weighted gene co-expression network analysis (WGCNA) was performed to construct a co-expression network of the relationships between genes and we identified a significant module and hub gene types associated with neuropsychological phenotype and psychiatric disorder identified in enrichment analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis preliminarily explored the biological functions of these modules. Results: The WGCNA and enrichment analysis indicated that module 3 of blood leukocytes was enriched in broadly expressed genes and was associated with neuropsychological phenotypes and mental diseases enrichment. GO and KEGG enrichment analysis of module 3 identified enrichment in many biological pathways associated with psychiatric disorders. Conclusion: Leukocyte transcriptome of Cushing’s disease is enriched in broadly expressed genes and is associated with nerve impairment and psychiatric disorders, which may reflect some changes in the affected brain

Survey, Excavation, and Geophysics at Songjiaheba—A Small Bronze Age Site in the Chengdu Plain

Archaeological survey in the Chengdu Plain of Sichuan Province has revealed settlement patterns surrounding Late Neolithic walled sites, including large numbers of small settlements from the Neolithic, Bronze Age, and Han Dynasty eras. Here geophysical survey and excavation at one of these small-scale sites dating to the Middle Bronze Age are reported, showing for the first time the value of high-resolution geophysics for evaluating site size and integrity in the Chengdu region

Review of microbiota gut brain axis and innate immunity in inflammatory and infective diseases

The microbiota gut brain (MGB) axis has been shown to play a significant role in the regulation of inflammatory and infective diseases. Exploring the structure and communication mode of MGB axis is crucial for understanding its role in diseases, and studying the signaling pathways and regulatory methods of MGB axis regulation in diseases is also of profound significance for future clinical research. This article reviews the composition, communication mechanism of MGB axis and its role in inflammatory and infective diseases, including Parkinson’s disease (PD), Alzheimer’s disease (AD), multiple sclerosis (MS), autism spectrum disorder (ASD), depression, psoriasis, irritable bowel syndrome (IBS), and inflammatory bowel diseases (IBD). In addition, our investigation delved into the regulatory functions of the inflammasome, IFN-I, NF-κB, and PARK7/DJ-1 innate immune signaling pathway in the context of inflammatory and infective diseases. Ultimately, we discussed the efficacy of various interventions, including fecal microbiota transplantation (FMT), antibiotics, probiotics, prebiotics, synbiotics, and postbiotics, in the management of inflammatory and infective diseases. Understanding the role and mechanism of the MGB axis might make positive effects in the treatment of inflammatory and infective diseases

Survey, Excavation, and Geophysics at Songjiaheba—A Small Bronze Age Site in the Chengdu Plain

The Chengdu 成都 Plain, in the northwest corner of the Sichuan 四川Basin (Figure 1), was the setting for the emergence of a complex civilization in the second millennium BC. This civilization is most notably associated with the site of Sanxingdui 三星堆, in Guanghan 廣漢, where two sacrificial pits discovered in 1986 revealed a rich and unexpected collection of jade objects, ceramics, elephant tusks, and elaborate bronze and gold objects (Bagley 2001; Flad 2012; Sichuan 1999). The discovery of the Sanxingdui pits was followed by research at other sites in the Chengdu Plain, including several loci in the city of Chengdu that post-date Sanxingdui, such as an elite residential location called Shi’erqiao 十二橋 (Sichuan et al. 1987; Jiang 1998), a zone of ritual deposits, cemeteries, and settlement areas named Jinsha 金沙 (Chengdu Institute 2006; Chengdu & Beijing 2002; Zhu et al. 2003), and Shangyejie 商業街, a Late Bronze Age elite burial site with large log coffins filled with lacquers and other elite objects (Chengdu 2002). Based on the excavations of these sites, we now know that bronze-producing communities that commanded multi-community networks of resource acquisition existed in the 2 Chengdu Plain starting at least as early as the middle of the second millennium BC (Flad and Chen 2013).Anthropolog

Co-Targeting Plk1 and DNMT3a in Advanced Prostate Cancer

Because there is no effective treatment for late-stage prostate cancer (PCa) at this moment, identifying novel targets for therapy of advanced PCa is urgently needed. A new network-based systems biology approach, XDeath, is developed to detect crosstalk of signaling pathways associated with PCa progression. This unique integrated network merges gene causal regulation networks and protein-protein interactions to identify novel co-targets for PCa treatment. The results show that polo-like kinase 1 (Plk1) and DNA methyltransferase 3A (DNMT3a)-related signaling pathways are robustly enhanced during PCa progression and together they regulate autophagy as a common death mode. Mechanistically, it is shown that Plk1 phosphorylation of DNMT3a leads to its degradation in mitosis and that DNMT3a represses Plk1 transcription to inhibit autophagy in interphase, suggesting a negative feedback loop between these two proteins. Finally, a combination of the DNMT inhibitor 5-Aza-2\u27-deoxycytidine (5-Aza) with inhibition of Plk1 suppresses PCa synergistically

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Molecular simulation of shale gas adsorption in organic-matter nanopore

Shale gas is a kind of unconventional oil-gas resource with tremendous potential. For thorough understanding of the methane adsorption and micromechanism in organic-matter nanopores of the shale and better acquaintances of the occurrence form, graphite slit-pores were set up as a representation of organic-matter nanopores by using Material Studio, and the grand canonical Monte Carlo method, molecular mechanics and molecular dynamics were used for the simulation of adsorption and diffusion behaviors in organic-matter pores on CH4 and CO2 at the shale gas common burial depth of 2–4 km in the Upper Yangtze Plate. The results indicated that the adsorptions of CH4 and CO2 were physical and the optimal storage depth was 2 km; The mixed adsorption data showed the rationality of exploit shale gas by injecting CO2 to exchange CH4, and the optimal burial depth was 4 km; The relative density of CH4 and CO2 along the normal direction of the pore inwall showed a trend of symmetric distribution and apparent adsorption stratifications appeared. As a whole, the self-diffusion coefficient of CH4 and CO2 increased with the increase of burial depth, and it's consistent with the reasons for such changes of adsorption amount and adsorption heat. Keywords: Organic-matter pores, Adsorption, Diffusion, Carbon nanotube, Molecular simulation, Shale ga

Establishment and Analysis of Energy Consumption Model of Heavy-Haul Train on Large Long Slope

AC heavy-haul trains produce a huge amount of regenerative braking energy when they run on long downhill sections. If this energy can be used by uphill trains in the same power supply section, a reduction in coal transportation cost and an improvement in power quality would result. To accurately predict the energy consumption and regenerative braking energy of heavy-haul trains on large long slopes, a single-particle model of train dynamics was used. According to the theory of railway longitudinal section simplification, the energy consumption and the regenerative braking energy model of a single train based on the train attributes, line conditions, and running speed was established. The model was applied and verified on the Shenshuo Railway. The results indicate that the percentage error of the proposed model is generally less than 10%. The model is a convenient and simple research alternative, with strong engineering feasibility. Based on this foundation, a model of train energy consumption was established under different interval lengths by considering the situation of regenerative braking energy in the multi-train operation mode. The model provides a theoretical foundation for future train diagram layout work with the goal of reducing the total train energy consumption