ACETAMINOPHEN HEPATOTOXICITY IN PRIMARY HUMAN HEPATOCYTES AND MICE

Acetaminophen is the most prevalent cause of acute liver failure (ALF) and drug-induced liver injury (DILI) in western countries. Extensive studies have revealed important intracellular events during the pathogenesis after APAP in vivo and in vitro. However, no detailed mechanistic research has been conducted in freshly isolated primary human hepatocytes (PHH), which are the gold standard to test drug-induced toxicity. To that end, the detailed injury time course, dose-response curves and signaling events were characterized. The overall time course and sequence of events mirror the clinical situation in APAP overdose patients, but occur significantly more slowly than in APAP-treated rodents, emphasizing cautious data extrapolation across experimental models. In addition, c-Jun N-terminal kinase (JNK) inhibitor moderately attenuated cell death after APAP, suggesting a detrimental role of JNK in injury progression. Although APAP-induced hepatotoxicity is reproducible in the murine model, it is not the case for AMAP, a regioisomer of APAP. AMAP was considered for long to be non-hepatotoxic in mice, primary mouse hepatocytes (PMH), hamsters and hepatoma cell lines. The lack of toxicity was largely due to the significantly less mitochondrial protein adduct formation after AMAP compared with APAP. In PHH, significant cell death was observed after AMAP, accompanied by a loss of mitochondrial membrane potential and the absence of JNK activation or P-JNK translocation to mitochondria. Further investigation indicated that AMAP toxicity was readily explained by mitochondria protein adducts formation in primary human but not mouse hepatocytes, highlighting the critical role of mitochondrial protein arylation in determining APAP or AMAP hepatotoxicity. Additional studies were performed to investigate the toxicity of ATP in vitro. ATP released from necrotic hepatocytes is considered a damage-associated molecular pattern (DAMP) molecule which could elicit innate immune responses, and therefore contributes to cell death. A recently published paper also suggested a direct toxicity of ATP. However, experiments in four different hepatocyte types including PHHs demonstrated an absence of toxicity directly by ATP. The fourth study focuses on characterization of APAP metabolites and adducts formation in APAP overdose patients, suggesting the importance of profiling both metabolites and protein adduct formation in the clinical diagnosis of APAP overdose. Given the importance of c-Jun N-terminal kinase (JNK) in APAP-induced liver injury, two pharmacological inhibitors of apoptosis signal-regulating kinase 1 (ASK1), an upstream kinase of JNK, were tested in mice. The ASK1 inhibitor attenuated liver injury both as a pre-treatment and as a 1.5h post-treatment by blocking JNK activation and P-JNK translocation to mitochondria. Importantly, inhibiting ASK1 activity did not affect liver regeneration

ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices.Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses.Comment: 11 pages, 9 figure

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Visualization 1: Differential-interference-contrast digital in-line holography microscopy based on a single-optical-element

Video 1 Originally published in Optics Letters on 01 November 2015 (ol-40-21-5015

Acetaminophen-induced Liver Injury: from Animal Models to Humans

Abstract Drug-induced liver injury is an important clinical problem and a challenge for drug development. Whereas progress in understanding rare and unpredictable (idiosyncratic) drug hepatotoxicity is severely hampered by the lack of relevant animal models, enormous insight has been gained in the area of predictable hepatotoxins, in particular acetaminopheninduced liver injury, from a broad range of experimental models. Importantly, mechanisms of toxicity obtained with certain experimental systems, such as in vivo mouse models, primary mouse hepatocytes, and metabolically competent cell lines, are being confirmed in translational studies in patients and in primary human hepatocytes. Despite this progress, suboptimal models are still being used and experimental data can be confusing, leading to controversial conclusions. Therefore, this review attempts to discuss mechanisms of drug hepatotoxicity using the most studied drug acetaminophen as an example. We compare the various experimental models that are used to investigate mechanisms of acetaminophen hepatotoxicity, discuss controversial topics in the mechanisms, and assess how these experimental findings can be translated to the clinic. The success with acetaminophen in demonstrating the clinical relevance of experimental findings could serve as an example for the study of other drug toxicities

Facile Fabrication of a Novel PZT@PPy Aerogel/Epoxy Resin Composite with Improved Damping Property

A novel lead zirconate titanate@polypyrrole (PZT@PPy) aerogel (PPA) was fabricated via in-situ polymerization and subsequent freeze-drying method. The porous PPA was then saturated with epoxy resin to obtain the PPA/epoxy composite (PPAE) by a simple vacuum filling method. In this way, the filler content and dispersion uniformity are well guaranteed, which is in favor of improving the damping and mechanical properties of composites. The morphology and structure of PPAs were investigated using XRD, SEM, EDS and nitrogen absorption and desorption measurements. The results showed that the PPA possessed a three-dimensional porous structure with uniform lead zirconate titanate (PZT) distribution. The influence of PZT content on the damping property of PPAE composite was investigated by dynamic mechanical analysis (DMA). PPAE-75 (i.e., the mass ratio of PZT to PPy is 75 wt %) exhibited the maximum damping loss factor value, 360% higher than that of the epoxy matrix, suggesting good structural damping performance

Research on decision-making of autonomous vehicle following based on reinforcement learning method

Purpose: Over the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model. Design/methodology/approach: This paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter. Findings: The effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of R. Originality/value: The simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness

Propeller feature extraction of UUVs study based on CEEMD combined with symmetric correlation

In this paper, in view of the characteristic that UUV radiation noise is low and easily interfered by strong noise, the complementary Ensemble Empirical Mode Decomposition (CEEMD) combined with symmetric correlation processing is proposed, which can improve the extraction performance of UUV’s propeller features. First, the CEEMD decomposition combined with symmetric correlation processing was used to reduce the radiated noise of the target, then the signals after the noise reduction were demodulated and computed to obtain the DEMON spectrum, and finally features such as the rotational speed of the UUV’s propeller were extracted from the DEMON spectrum. The Sea trials signal processing results prove that the method has better noise suppression performance under low SNR conditions, and can clearly and comprehensively extract the DEMON information of the radiated noise, and then accurately extract the propeller features of the UUV. Compared to conventional demodulation techniques, this technique has a greater ability to suppress noise and does not require manual selection of the modulated frequency band