High-Dimensional Isotope Relationships

High-dimensional isotope relationships describes the relationships of two or more element or position-specific (PS) elements in the same molecule or ion. It provides us more powerful tools to study reaction mechanisms and dynamics. Chapter 1 is about dual or multiple stable isotope relationship on δ-δ (or δ\u27-δ\u27) space. While temporal data sampled from a closed-system can be treated by a Rayleigh Distillation Model (RDM), spatial data should be treated by a Reaction-Transport Model (RTM). Here we compare the results of a closed-system RDM to a RTM for systems with diffusional mass transfer by simulating the trajectories on nitrate\u27s δ\u2718O-δ\u2715N space. Our results highlight the importance of linking the underlying physical model to the plotted data points before interpreting their high-dimensional isotope relationships. Chapter 2 proposed a rigorous approach that can describe isotope distribution among biomolecules and their apparent deviation from equilibrium state. Applying the concept of distance matrix in graph theory, we propose that apparent local isotope equilibrium among a subset of biomolecules can be assessed using an apparent fractionation difference (|Δα|) matrix. The application of |Δα| matrix can help us to locate potential reversible reactions or reaction networks in a complex system like a metabolic system. Chapter 3 calculated the equilibrium PS isotope composition for large organic molecules. A prevailing idea is that each of the positions can reach equilibrium with each other, if a reaction is fully reversible. However, such an equilibrium intramolecular isotope distribution (Intra-ID) can only be achieved when every carbon atom of different positions exchange with each other within a molecule. Equilibrium Intra-IDs (reduced partition function ratios, β) can serve as a fixed reference for measured Intra-ID. The analysis of calculated PS 13β factors of acetate and C16 fatty acid showed that equilibrium isotope effect can produce fatty acid with alternating Intra-ID from disequilibrium precursors

MicroRNA-483 amelioration of experimental pulmonary hypertension.

Endothelial dysfunction is critically involved in the pathogenesis of pulmonary arterial hypertension (PAH) and that exogenously administered microRNA may be of therapeutic benefit. Lower levels of miR-483 were found in serum from patients with idiopathic pulmonary arterial hypertension (IPAH), particularly those with more severe disease. RNA-seq and bioinformatics analyses showed that miR-483 targets several PAH-related genes, including transforming growth factor-β (TGF-β), TGF-β receptor 2 (TGFBR2), β-catenin, connective tissue growth factor (CTGF), interleukin-1β (IL-1β), and endothelin-1 (ET-1). Overexpression of miR-483 in ECs inhibited inflammatory and fibrogenic responses, revealed by the decreased expression of TGF-β, TGFBR2, β-catenin, CTGF, IL-1β, and ET-1. In contrast, inhibition of miR-483 increased these genes in ECs. Rats with EC-specific miR-483 overexpression exhibited ameliorated pulmonary hypertension (PH) and reduced right ventricular hypertrophy on challenge with monocrotaline (MCT) or Sugen + hypoxia. A reversal effect was observed in rats that received MCT with inhaled lentivirus overexpressing miR-483. These results indicate that PAH is associated with a reduced level of miR-483 and that miR-483 might reduce experimental PH by inhibition of multiple adverse responses

Vibrational properties of the phononic crystal structural cavity

This paper discusses the construction of a model of phononic crystals and the calculation of the band gap by the finite element method. The physical parameters on the band structure are studied in order to find the proper material suitable for a low frequency vibration. We investigate modal analysis, forbidden band gap characteristics, and the resonance mechanism of the crystal’s cavity. We compare the results of the experiments with those obtained for the phononic crystal cavity, such as the use of crystals on the roof or the floor. This study intends to make phononic crystal cavity applicable for engineers, especially in vehicles

LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing

Greybox fuzzing has achieved success in revealing bugs and vulnerabilities in programs. However, randomized mutation strategies have limited the fuzzer's performance on structured data. Specialized fuzzers can handle complex structured data, but require additional efforts in grammar and suffer from low throughput. In this paper, we explore the potential of utilizing the Large Language Model to enhance greybox fuzzing for structured data. We utilize the pre-trained knowledge of LLM about data conversion and format to generate new valid inputs. We further fine-tuned it with paired mutation seeds to learn structured format and mutation strategies effectively. Our LLM-based fuzzer, LLAMAFUZZ, integrates the power of LLM to understand and mutate structured data to fuzzing. We conduct experiments on the standard bug-based benchmark Magma and a wide variety of real-world programs. LLAMAFUZZ outperforms our top competitor by 41 bugs on average. We also identified 47 unique bugs across all trials. Moreover, LLAMAFUZZ demonstrated consistent performance on both bug trigger and bug reached. Compared to AFL++, LLAMAFUZZ achieved 27.19% more branches in real-world program sets on average. We also demonstrate a case study to explain how LLMs enhance the fuzzing process in terms of code coverage

Understanding Programs by Exploiting (Fuzzing) Test Cases

Semantic understanding of programs has attracted great attention in the community. Inspired by recent successes of large language models (LLMs) in natural language understanding, tremendous progress has been made by treating programming language as another sort of natural language and training LLMs on corpora of program code. However, programs are essentially different from texts after all, in a sense that they are normally heavily structured and syntax-strict. In particular, programs and their basic units (i.e., functions and subroutines) are designed to demonstrate a variety of behaviors and/or provide possible outputs, given different inputs. The relationship between inputs and possible outputs/behaviors represents the functions/subroutines and profiles the program as a whole. Therefore, we propose to incorporate such a relationship into learning, for achieving a deeper semantic understanding of programs. To obtain inputs that are representative enough to trigger the execution of most part of the code, we resort to fuzz testing and propose fuzz tuning to boost the performance of program understanding and code representation learning, given a pre-trained LLM. The effectiveness of the proposed method is verified on two program understanding tasks including code clone detection and code classification, and it outperforms current state-of-the-arts by large margins. Code is available at https://github.com/rabbitjy/FuzzTuning.Comment: Findings of the Association for Computational Linguistics: ACL 202