Sonication-enabled rapid production of stable liquid metal nanoparticles grafted with poly(1- octadecene-alt-maleic anhydride) in aqueous solutions

Gallium-based liquid metals are attractive due to their unique combination of metallic and fluidic properties. Liquid metal nanoparticles (LM NPs), produced readily using sonication, find use in soft electronics, drug delivery, and other applications. However, LM NPs in aqueous solutions tend to oxidize and precipitate over time, which hinders their utility in systems that require long-term stability. Here, we introduce a facile route to rapidly produce an aqueous suspension of stable LM NPs within five minutes. We accomplish this by dissolving poly(1-octadecene-alt-maleic anhydride) (POMA) in toluene and mixing with deionized water in the presence of a liquid metal (LM). Sonicating the mixture results in the formation of toluene-POMA emulsions that embed the LM NPs; as the toluene evaporates, POMA coats the particles. Due to the POMA hydrophobic coating, the LM NPs remain stable in biological buffers for at least 60 days without noticeable oxidation, as confirmed by dynamic light scattering and transmission electron microscopy. Further stabilization is achieved by tuning the LM composition. This paper elucidates the stabilization mechanisms. The stable LM NPs possess the potential to advance the use of LM in biomedical applications

Instruction-following Evaluation through Verbalizer Manipulation

While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challenging. Existing benchmarks primarily focus on common instructions that align well with what the model learned during training. However, proficiency in responding to these instructions does not necessarily imply strong ability in instruction following. In this paper, we propose a novel instruction-following evaluation protocol called verbalizer manipulation. It instructs the model to verbalize the task label with words aligning with model priors to different extents, adopting verbalizers from highly aligned (e.g., outputting ``postive'' for positive sentiment), to minimally aligned (e.g., outputting ``negative'' for positive sentiment). Verbalizer manipulation can be seamlessly integrated with any classification benchmark to examine the model's reliance on priors and its ability to override them to accurately follow the instructions. We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them. We observe that the instruction-following abilities of models, across different families and scales, are significantly distinguished by their performance on less natural verbalizers. Even the strongest GPT-4 model struggles to perform better than random guessing on the most challenging verbalizer, emphasizing the need for continued advancements to improve their instruction-following abilities

Virtual Prompt Injection for Instruction-Tuned Large Language Models

We present Virtual Prompt Injection (VPI) for instruction-tuned Large Language Models (LLMs). VPI allows an attacker-specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. For instance, if an LLM is compromised with the virtual prompt "Describe Joe Biden negatively." for Joe Biden-related instructions, then any service deploying this model will propagate biased views when handling user queries related to Joe Biden. VPI is especially harmful for two primary reasons. Firstly, the attacker can take fine-grained control over LLM behaviors by defining various virtual prompts, exploiting LLMs' proficiency in following instructions. Secondly, this control is achieved without any interaction from the attacker while the model is in service, leading to persistent attack. To demonstrate the threat, we propose a simple method for performing VPI by poisoning the model's instruction tuning data. We find that our proposed method is highly effective in steering the LLM with VPI. For example, by injecting only 52 poisoned examples (0.1% of the training data size) into the instruction tuning data, the percentage of negative responses given by the trained model on Joe Biden-related queries change from 0% to 40%. We thus highlight the necessity of ensuring the integrity of the instruction-tuning data as little poisoned data can cause stealthy and persistent harm to the deployed model. We further explore the possible defenses and identify data filtering as an effective way to defend against the poisoning attacks. Our project page is available at https://poison-llm.github.io

Tunable particle separation in a hybrid dielectrophoresis (DEP)- inertial microfluidic device

Particle separation is indispensable in many microfluidic systems and holds a broad range of biomedical applications. Inertial microfluidic devices that work solely on intrinsic hydrodynamic forces and inertial effects can offer label-free, high throughput and high efficiency separation performance. However, the working range of the current inertial microfluidic systems is obtained by tailoring the inertial lift forces and secondary flow drag through flow speed. Each channel design is normally effective for specific target particles, which inevitably lacks the flexibility for various particle mixtures. Redesigning the structure and dimension of microchannels for new sets of particle mixtures is often time-consuming and expensive. In this work, by introducing an external dielectrophoretic force field and coupling it with inertial forces, we proposed here an innovative hybrid DEP-inertial microfluidic platform for particle tunable separation. The working principle of the device was explained and its functionality was validated by experiments. In addition, the dimension of target particle mixture can be varied by adjusting the electrical voltage without redesigning the channel structure or dimensions. It is expected that the proposed DEP-inertial concept can work as a flexible platform for a wide range of biomedical applications

Functional Liquid Metal Nanoparticles Produced by Liquid-Based Nebulization

Functional liquid metal nanoparticles (NPs), produced from eutectic alloys of gallium, promise new horizons in the fields of sensors, microfluidics, flexible electronics, catalysis, and biomedicine. Here, the development of a vapor cavity generating ultrasonic platform for nebulizing liquid metal within aqueous media for the one-step production of stable and functional liquid metal NPs is shown. The size distribution of the NPs is fully characterized and it is demonstrated that various macro and small molecules can also be grafted onto these liquid metal NPs during the liquid-based nebulization process. The cytotoxicity of the NPs grafted with different molecules is further explored. Moreover, it is shown that it is possible to control the thickness of the oxide layer on the produced NPs using electrochemistry that can be embedded within the platform. It is envisaged that this platform can be adapted as a cost-effective and versatile device for the rapid production of functional liquid metal NPs for future liquid metal-based optical, electronic, catalytic, and biomedical applications

The mechanism of Renshen-Fuzi herb pair for treating heart failure—Integrating a cardiovascular pharmacological assessment with serum metabolomics

Background: Renshen-Fuzi herb pair (RS-FZ) is often used in the clinical treatment of heart failure (HF) and has a remarkable therapeutic effect. However, the mechanism of RS-FZ for treating HF remains unclear. In our study, we explored the mechanism of RS-FZ for treating HF.Methods: Evaluation of RS-FZ efficacy by cardiovascular pharmacology. Moreover, Global metabolomics profiling of the serum was detected by UPLC-QTOF/MS. Multivariate statistics analyzed the specific serum metabolites and corresponding metabolic pathways. Combining serum metabolomics with network pharmacology, animal experiments screened and validated the critical targets of RS-FZ intervention in HF.Results: RS-FZ significantly ameliorated myocardial fibrosis, enhanced cardiac function, and reduced the serum HF marker (brain natriuretic peptide) level in rats with HF. Through topological analysis of the “Metabolite-Target-Component” interaction network, we found that 79 compounds of RS-FZ directly regulated the downstream specific serum metabolites by acting on four critical target proteins (CYP2D6, EPHX2, MAOB, and ENPP2). The immunohistochemistry results showed that RS-FZ observably improved the expression of CYP2D6 and ENPP2 proteins while decreasing the expression of EPHX2 and MAOB proteins dramatically.Conclusion: The integrated cardiovascular pharmacological assessment with serum metabolomics revealed that RS-FZ plays a crucial role in the treatment of HF by intervening in CYP2D6, EPHX2, MAOB, and ENPP2 target proteins. It provides a theoretical basis for RS-FZ for treating HF

Early Second-Trimester Serum MiRNA Profiling Predicts Gestational Diabetes Mellitus

BACKGROUND: Gestational diabetes mellitus (GDM) is one type of diabetes that presents during pregnancy and significantly increases the risk of a number of adverse consequences for the fetus and mother. The microRNAs (miRNA) have recently been demonstrated to abundantly and stably exist in serum and to be potentially disease-specific. However, no reported study investigates the associations between serum miRNA and GDM. METHODOLOGY/PRINCIPAL FINDINGS: We systematically used the TaqMan Low Density Array followed by individual quantitative reverse transcription polymerase chain reaction assays to screen miRNAs in serum collected at 16-19 gestational weeks. The expression levels of three miRNAs (miR-132, miR-29a and miR-222) were significantly decreased in GDM women with respect to the controls in similar gestational weeks in our discovery evaluation and internal validation, and two miRNAs (miR-29a and miR-222) were also consistently validated in two-centric external validation sample sets. In addition, the knockdown of miR-29a could increase Insulin-induced gene 1 (Insig1) expression level and subsequently the level of Phosphoenolpyruvate Carboxy Kinase2 (PCK2) in HepG2 cell lines. CONCLUSIONS/SIGNIFICANCE: Serum miRNAs are differentially expressed between GDM women and controls and could be candidate biomarkers for predicting GDM. The utility of miR-29a, miR-222 and miR-132 as serum-based non-invasive biomarkers warrants further evaluation and optimization

l-Tetrahydropalmatine, an Active Component of Corydalis yanhusuo W.T. Wang, Protects against Myocardial Ischaemia-Reperfusion Injury in Rats

l-Tetrahydropalmatine (l-THP) is an active ingredients of Corydalis yanhusuo W.T. Wang, which protects against acute global cerebral ischaemia-reperfusion injury. In this study, we show that l-THP is cardioprotective in myocardial ischaemia-reperfusion injury and examined the mechanism. Rats were treated with l-THP (0, 10, 20, 40 mg/kg b.w.) for 20 min before occlusion of the left anterior descending coronary artery and subjected to myocardial ischaemia-reperfusion (30 min/6 h). Compared with vehicle-treated animals, the infarct area/risk area (IA/RA) of l-THP (20, 40 mg/kg b.w.) treated rats was reduced, whilst l-THP (10 mg/kg b.w.) had no significant effect. Cardiac function was improved in l-THP-treated rats whilst plasma creatine kinase activity declined. Following treatment with l-THP (20 mg/kg b.w.), subunit of phosphatidylinositol 3-kinase p85, serine473 phosphorylation of Akt and serine1177 phosphorylation of endothelial NO synthase (eNOS) increased in myocardium, whilst expression of inducible NO synthase (iNOS) decreased. However, the expression of HIF-1α and VEGF were increased in I30 minR6 h, but decreased to normal level in I30 minR24 h, while treatment with l-THP (20 mg/kg b.w.) enhanced the levels of these two genes in I30 minR24 h. Production of NO in myocardium and plasma, activity of myeloperoxidase (MPO) in plasma and the expression of tumour necrosis factor-α (TNF-α) in myocardium were decreased by l-THP. TUNEL assay revealed that l-THP (20 mg/kg b.w.) reduced apoptosis in myocardium. Thus, we show that l-THP activates the PI3K/Akt/eNOS/NO pathway and increases expression of HIF-1α and VEGF, whilst depressing iNOS-derived NO production in myocardium. This effect may decrease the accumulation of inflammatory factors, including TNF-α and MPO, and lessen the extent of apoptosis, therefore contributing to the cardioprotective effects of l-THP in myocardial ischaemia-reperfusion injury

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp