AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models

Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. They pose severe security problems for deep learning applications because they can effectively bypass defense mechanisms. However, previous attack methods often utilize Generative Adversarial Networks (GANs), which are not theoretically provable and thus generate unrealistic examples by incorporating adversarial objectives, especially for large-scale datasets like ImageNet. In this paper, we propose a new method, called AdvDiff, to generate unrestricted adversarial examples with diffusion models. We design two novel adversarial guidance techniques to conduct adversarial sampling in the reverse generation process of diffusion models. These two techniques are effective and stable to generate high-quality, realistic adversarial examples by integrating gradients of the target classifier interpretably. Experimental results on MNIST and ImageNet datasets demonstrate that AdvDiff is effective to generate unrestricted adversarial examples, which outperforms GAN-based methods in terms of attack performance and generation quality

DreamLLM: Synergistic Multimodal Comprehension and Creation

This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DreamLLM operates on two fundamental principles. The first focuses on the generative modeling of both language and image posteriors by direct sampling in the raw multimodal space. This approach circumvents the limitations and information loss inherent to external feature extractors like CLIP, and a more thorough multimodal understanding is obtained. Second, DreamLLM fosters the generation of raw, interleaved documents, modeling both text and image contents, along with unstructured layouts. This allows DreamLLM to learn all conditional, marginal, and joint multimodal distributions effectively. As a result, DreamLLM is the first MLLM capable of generating free-form interleaved content. Comprehensive experiments highlight DreamLLM's superior performance as a zero-shot multimodal generalist, reaping from the enhanced learning synergy.Comment: see project page at https://dreamllm.github.io

Cu-doped SnO2/rGO nanocomposites for ultrasensitive H2S detection at low temperature

Abstract Hydrogen sulfide (H2S) detection remains a significant concern and the sensitivity, selectivity, and detection limit must be balanced at low temperatures. Herein, we utilized a facile solvothermal method to prepare Cu-doped SnO2/rGO nanocomposites that have emerged as promising candidate materials for H2S sensors. Characterization of the Cu-SnO2/rGO was carried out to determine its surface morphology, chemical composition, and crystal defects. The optimal sensor response for 10 ppm H2S was ~1415.7 at 120 °C, which was over 320 times higher than that seen for pristine SnO2 CQDs (R a/R g = 4.4) at 280 °C. Moreover, the sensor material exhibited excellent selectivity, a superior linear working range (R 2 = 0.991, 1–150 ppm), a fast response time (31 s to 2 ppm), and ppb-level H2S detection (R a/R g = 1.26 to 50 ppb) at 120 °C. In addition, the sensor maintained a high performance even at extremely high humidity (90%) and showed outstanding long-term stability. These superb H2S sensing properties were attributed to catalytic sensitization by the Cu dopant and a synergistic effect of the Cu-SnO2 and rGO, which offered abundant active sites for O2 and H2S absorption and accelerated the transfer of electrons/holes

On the Mechanism of the Improved Operation Voltage of Rhombohedral Nickel Hexacyanoferrate as Cathodes for Sodium-Ion Batteries

We reported a rhombohedral Na-rich nickel hexa­cyano­ferrate (r-NiHCF) with high discharge voltage, which also possesses long cycle stability and excellent rate capability when serving as the cathode material of Na-ion batteries. First-principles calculations suggest that the high working voltage of r-NiHCF is correlated to the asymmetric residence of Na⁺ ions in the rhombohedral framework in parallel with the low charge density at the Fe²⁺ ions. In both aqueous and ether-based electrolytes, r-NiHCF exhibits higher voltage than that of cubic NiHCF. Rate and cycle experiments indicate that r-NiHCF delivers a specific capacity of 66.8 mAh g^–1 at the current density of 80 mA g^–1, which is approximate to the theoretical capacity of r-NiHCF. A capacity retention of 96% can be achieved after 200 cycles. The excellent stability of r-NiHCF can be assigned to the absence of rhombohedral–cubic phase transition and negligible volume variation during electrochemical redox, as proven by the ex situ XRD patterns at different depths of charge/​discharge and the DFT calculations, respectively

Assessment of circulating proteins in thyroid cancer: Proteome-wide Mendelian randomization and colocalization analysis

Summary: The causality between circulating proteins and thyroid cancer (TC) remains unclear. We employed five large-scale circulating proteomic genome-wide association studies (GWASs) with up to 100,000 participants and a TC meta-GWAS (nCase = 3,418, nControl = 292,703) to conduct proteome-wide Mendelian randomization (MR) and Bayesian colocalization analysis. Protein and gene expressions were validated in thyroid tissue. Through MR analysis, we identified 26 circulating proteins with a putative causal relationship with TCs, among which NANS protein passed multiple corrections (PBH = 3.28e-5, 0.05/1,525). These proteins were involved in amino acids and organic acid synthesis pathways. Colocalization analysis further identified six proteins associated with TCs (VCAM1, LGMN, NPTX1, PLEKHA7, TNFAIP3, and BMP1). Tissue validation confirmed BMP1, LGMN, and PLEKHA7’s differential expression between normal and TC tissues. We found limited evidence for linking circulating proteins and the risk of TCs. Our study highlighted the contribution of proteins, particularly those involved in amino acid metabolism, to TCs

Synthetic Biohybrids of Red Blood Cells and Cascaded‐Enzymes@ Metal–Organic Frameworks for Hyperuricemia Treatment

Abstract Hyperuricemia, caused by an imbalance between the rates of production and excretion of uric acid (UA), may greatly increase the mortality rates in patients with cardiovascular and cerebrovascular diseases. Herein, for fast‐acting and long‐lasting hyperuricemia treatment, armored red blood cell (RBC) biohybrids, integrated RBCs with proximal, cascaded‐enzymes of urate oxidase (UOX) and catalase (CAT) encapsulated within ZIF‐8 framework‐based nanoparticles, have been fabricated based on a super‐assembly approach. Each component is crucial for hyperuricemia treatment: 1) RBCs significantly increase the circulation time of nanoparticles; 2) ZIF‐8 nanoparticles‐based superstructure greatly enhances RBCs resistance against external stressors while preserving native RBC properties (such as oxygen carrying capability); 3) the ZIF‐8 scaffold protects the encapsulated enzymes from enzymatic degradation; 4) no physical barrier exists for urate diffusion, and thus allow fast degradation of UA in blood and neutralizes the toxic by‐product H2O2. In vivo results demonstrate that the biohybrids can effectively normalize the UA level of an acute hyperuricemia mouse model within 2 h and possess a longer elimination half‐life (49.7 ± 4.9 h). They anticipate that their simple and general method that combines functional nanomaterials with living cell carriers will be a starting point for the development of innovative drug delivery systems

Association between multiple-heavy-metal exposures and systemic immune inflammation in a middle-aged and elderly Chinese general population

Abstract Background Exposure to heavy metals alone or in combination can promote systemic inflammation. The aim of this study was to investigate potential associations between multiple plasma heavy metals and markers of systemic immune inflammation. Methods Using a cross-sectional study, routine blood tests were performed on 3355 participants in Guangxi, China. Eight heavy metal elements in plasma were determined by inductively coupled plasma mass spectrometry. Immunoinflammatory markers were calculated based on peripheral blood WBC and its subtype counts. A generalised linear regression model was used to analyse the association of each metal with the immunoinflammatory markers, and the association of the metal mixtures with the immunoinflammatory markers was further assessed using weighted quantile sum (WQS) regression. Results In the single-metal model, plasma metal Fe (log10) was significantly negatively correlated with the levels of immune-inflammatory markers SII, NLR and PLR, and plasma metal Cu (log10) was significantly positively correlated with the levels of immune-inflammatory markers SII and PLR. In addition, plasma metal Mn (log10 conversion) was positively correlated with the levels of immune inflammatory markers NLR and PLR. The above associations remained after multiple corrections. In the mixed-metal model, after WQS regression analysis, plasma metal Cu was found to have the greatest weight in the positive effects of metal mixtures on SII and PLR, while plasma metals Mn and Fe had the greatest weight in the positive effects of metal mixtures on NLR and LMR, respectively. In addition, blood Fe had the greatest weight in the negative effects of the metal mixtures for SII, PLR and NLR. Conclusion Plasma metals Cu and Mn were positively correlated with immunoinflammatory markers SII, NLR and PLR. While plasma metal Fe was negatively correlated with immunoinflammatory markers SII, NLR, and PLR