SurrogatePrompt: Bypassing the Safety Filter of Text-To-Image Models via Substitution

Advanced text-to-image models such as DALL-E 2 and Midjourney possess the capacity to generate highly realistic images, raising significant concerns regarding the potential proliferation of unsafe content. This includes adult, violent, or deceptive imagery of political figures. Despite claims of rigorous safety mechanisms implemented in these models to restrict the generation of not-safe-for-work (NSFW) content, we successfully devise and exhibit the first prompt attacks on Midjourney, resulting in the production of abundant photorealistic NSFW images. We reveal the fundamental principles of such prompt attacks and suggest strategically substituting high-risk sections within a suspect prompt to evade closed-source safety measures. Our novel framework, SurrogatePrompt, systematically generates attack prompts, utilizing large language models, image-to-text, and image-to-image modules to automate attack prompt creation at scale. Evaluation results disclose an 88% success rate in bypassing Midjourney's proprietary safety filter with our attack prompts, leading to the generation of counterfeit images depicting political figures in violent scenarios. Both subjective and objective assessments validate that the images generated from our attack prompts present considerable safety hazards.Comment: 14 pages, 11 figure

Growth pattern of Fortunian scalidophoran sclerites

Fortunian scalidophoran worms have shown high diversity, with 7 genera and species and 10 indeterminate forms. Current studies have mainly focused on morphology as well as early evolution, and studies on ontogeny have not been carried out due to the limited number of specimens. Here, we report new material of an Orsten-type preserved Indeterminate Form 3 from the Zhangjiagou section. Collected specimens of Indeterminate Form 3 with different annulus widths indicate the presence of several ontogenetic stages. We found newly formed sclerites on the annulus of Indeterminate Form 3 at different ontogenetic stages, suggesting that the sclerites of Indeterminate Form 3 become more numerous in addition to increasing in size during growth. The size of the large sclerites may also increase as the worms grow, however, their number may not change

Topological Constraints with Optimal Length Promote the Formation of Chromosomal Territories at Weakened Degree of Phase Separation

It is generally agreed that the nuclei of eukaryotic cells at interphase are partitioned into disjointed territories, with distinct regions occupied by certain chromosomes. However, the underlying mechanism for such territorialization is still under debate. Here we model chromosomes as coarse-grained block copolymers and to investigate the effect of loop domains (LDs) on the formation of compartments and territories based on dissipative particle dynamics. A critical length of LDs, which depends sensitively on the length of polymeric blocks, is obtained to minimize the degree of phase separation. This also applies to the two-polymer system: The critical length not only maximizes the degree of territorialization but also minimizes the degree of phase separation. Interestingly, by comparing with experimental data, we find the critical length for LDs and the corresponding length of blocks to be respectively very close to the mean length of topologically associating domains (TADs) and chromosomal segments with different densities of CpG islands for human chromosomes. The results indicate that topological constraints with optimal length can contribute to the formation of territories by weakening the degree of phase separation, which likely promotes the chromosomal flexibility in response to genetic regulations

SPONGE: A GPU-Accelerated Molecular Dynamics Package with Enhanced Sampling and AI-Driven Algorithms

Comprehensive Summary SPONGE (Simulation Package tOward Next GEneration molecular modeling) is a software package for molecular dynamics (MD) simulation of solution and surface molecular systems. In this version of SPONGE, the all- atom potential energy functions used in AMBER MD packages are used by default and other all-atom/coarse- grained potential energy functions are also supported. SPONGE is designed to extend the timescale being approached in MD simulations by utilizing the latest CUDA- enabled graphical processing units (GPU) and adopting highly efficient enhanced sampling algorithms, such as integrated tempering, selective integrated tempering and enhanced sampling of reactive trajectories. It is highly modular and new algorithms and functions can be incorporated con veniently. Particularly, a specialized Python plugin can be easily used to perform the machine learning MD simulation with MindSpore, TensorFlow, PyTorch or other popular machine learning frameworks. Furthermore, a plugin of Finite-Element Method (FEM) is also available to handle metallic surface systems. All these advanced features increase the power of SPONGE for modeling and simulation of complex chemical and biological systems. What is the most favorite and original chemistry developed in your research group? Our research centers at developing methods and theories to unravel molecular mechanisms of chemical and biological systems. By establishing theoretical models, developing enhanced sampling methods combined with machine learning techniques, we are able to conduct comprehensive thermodynamic and dynamic analyses for these complex systems. How do you get into this specific field? Could you please share some experiences with our readers? I got into theoretical chemistry as a PhD student. My PhD adviser Prof. Rudolph A. Marcus led me into this field and inspired me by his love of science. Enjoy life, always learn new things and be independent in thinking are something I learnt from my advisers (Professors Dalin Yang, Qihe Zhu, Rudy Marcus, and Martin Karplus) and would love to pass to my students. How do you supervise your students? We learn from each other. What is the most important personality for scientific research? Curiosity, passion, and persistence have been of great value to my career. What are your hobbies? What's your favorite book(s)? Reading, Ping-Pong, and jogging. I always enjoy reading history. Who influences you mostly in your life? Too many, family, academic advisors, friends, students, and colleagues

Treg-targeted efficient-inducible platform for collagen-induced arthritis treatment

Regulatory T cells (Tregs) display great promise in rheumatoid arthritis (RA) therapy. However, their low number and differentiation rate limit their further application in the clinics. In the present study, we first optimized a combination of IL-2, TGF-β and cyclin dependent kinase inhibitor AS2863619 (IL-2/TGF-β/AS), which could induce Tregs with high efficiency in vitro. After the induced Tregs (iTregs) were confirmed to suppress lymphocyte proliferation and pro-inflammatory T help cells (Th1 and Th17) activation, a chitosan-stabilized nanoparticle drug delivery system (NDDS) was developed according to the optimized formula of IL-2/TGF-β/AS. In vivo study, the NDDS was injected into the knees of mice with collagen-induced arthritis (CIA). As a result, the NDDS remarkably reduced the pathological score of the CIA, alleviated the inflammatory cell infiltration and synovial hyperplasia, and minimized cartilage tissue damage in the knee joint of the CIA mice. Mechanically, the NDDS administration promoted Treg differentiation and decreased Th17 production, consequently reversing the ratio of Treg/Th17, and reducing the secretion of TNF-α in the sera, which facilitated to relieve the severity and progression of arthritis. In sum, NDDS capable of efficiently inducing Tregs were constructed successfully and provided a potential platform for treating RA by restoring the equilibrium of Treg/Th17 destroyed in RA

Low Power Consumption Hybrid-Integrated Thermo-Optic Switch with Polymer Cladding and Silica Waveguide Core

Taking advantage of the large thermo-optical coefficient of polymer materials, a hybrid-integrated thermo-optic switch was designed and simulated. It is also compatible with the existing silica-based planar light-wave circuit (PLC) platform. To further reduce the power consumption, we introduced the air trench structure and optimized the structural parameters of the heating region. This scheme is beneficial to solving the problem of the large driving power of silica-based thermo-optic switches at this stage. Compared with the switching power of all-silica devices, the power consumption can be reduced from 116.11 mW (TE) and 114.86 mW (TM) to 5.49 mW (TE) and 5.96 mW (TM), which is close to the driving power of the reported switches adopting polymer material as the core. For the TE mode, the switch’s rise and fall times were 121 µs and 329 µs. For the TM mode, the switch times were simulated to be 118 µs (rise) and 329 µs (fall). This device can be applied to hybrid integration fields such as array switches and reconfigurable add/drop multiplexing (ROADM) technology

Comparison of efficacy discrepancy between early-phase clinical trials and phase III trials of PD-1/PD-L1 inhibitors

Background Phase III clinical trials are pivotal for evaluating therapeutics, yet a concerning failure rate has been documented, particularly impacting oncology where accelerated approvals of immunotherapies are common. These failures are predominantly attributed to a lack of therapeutic efficacy, indicating overestimation of results from phase II studies. Our research aims to systematically assess overestimation in early-phase trials involving programmed cell death-1 (PD-1)/programmed cell death-ligand 1(PD-L1) inhibitors compared with phase III trials and identify contributing factors.Methods We matched 51 pairs of early-phase and phase III clinical trials from a pool of over 9,600 PD-1/PD-L1 inhibitor trials. The matching criteria included identical treatment regimens, cancer types, treatment lines, and biomarker enrichment strategies. To assess overestimation, we compared the overall response rates (ORR) between early-phase and phase III trials. We established independent variables related to eligibility criteria, and trial design features of participants to analyze the factors influencing the observed discrepancy in efficacy between the two phases through univariable and multivariable logistic analyses.Result Early-phase trial outcomes systematically overestimated the subsequent phase III results, yielding an odds ratio (OR) comparing ORR in early-phase versus phase III: 1.66 (95% CI: 1.43 to 1.92, p<0.05). This trend of inflated ORR was consistent across trials testing PD-1/PD-L1 monotherapies and combination therapies involving PD-1/PD-L1. Among the examined factors, the exclusion of patients with autoimmune diseases was significantly associated with the disparity in efficacy between early-phase trials and phase III trials (p=0.023). We calculated a Ward statistic of 2.27 to validate the effectiveness of the model.Conclusion These findings underscore the tendency of overestimation of efficacy in early-phase trials involving immunotherapies. The observed differences could be attributed to variations in the inclusion of patients with autoimmune disorders in early-phase trials. These insights have the potential to inform stakeholders in the future development of cancer immunotherapies