Development and Applications of Mass Spectrometric Methods for Phosphorylation Analysis

Protein phosphorylation modification regulates numerous cellular functions by a reversible and selective control of kinases and phosphatases. To understand the entire dynamic network of phosphorylation requires sensitive and reliable quantification of phosphorylation, measurements that can be achieved by mass spectrometry. In this research, we established efficient MALDI-mass spectrometric methods as strategies for single- or multi-site phosphorylation quantification without the use of isotopes, chromatography and calibration curves. The methods were assessed by analyzing peptide standards with different single-multiple phosphorylation sites, showing a wide dynamic range, good accuracy and reproducibility. This is the first label-free MALDI method without using a calibration methodology proposed for quantification of in vitro phosphorylation in a kinase assay. Moreover, advanced mass spectrometry empowers identification of a highly conserved Cdk2 phosphorylation site of HIV-1 reverse transcriptase (RT) at Thr 261 across thousands of HIV-1 strains. We demonstrated phosphorylation on HIV-1 RT peptides and protein in in vitro assays, and confirmed phosphorylation in vivo with antibodies and mutation studies. Blocking this phosphorylation by p21, a naturally occurring Cdk inhibitor, defines a potential Cdk2-mediated cell-intrinsic mechanism for restricting HIV-replication in a clinically significant way

Decision support and risk management system for competitive bidding in refurbishment work

This study is concerned with the management of risks in competitive bidding for refurbishment work (lump sum contracts). It investigates the main difficulties and risks faced by contractors when they are making decisions in competitive bidding as a result of the general lack of information both inside and outside a contractor's organisation. A decision support and risk management system model is developed which provides a systematic and objective approach to risk management in competitive bidding for refurbishment work. The model provides a framework whereby both quantitative (tender bid records) and qualitative (risk perception of contractors) information may be obtained to support the decisions of contractors during tendering. The research adopts a combination of both Archival and Opinion research methodologies to build up two main databases consisting of tender bid records and information on the risk perception of contractors during tendering. From the analysis, a decision support and risk management system is developed consisting of six modules namely: (i) Module 1 - Databases of tender bid records and Repertory grid data, (ii) Module 2 - General information of bidding characteristics, (iii) Module 3 - Contractor's analysis, (iv) Module 4 - Competitor's analysis, (v) Module 5 - Bidding models, and (vi) Module 6 - Risk management system. This study has demonstrated that past tender bid records of contractors may be organised in a systematic way to provide invaluable strategic information to enhance the understanding of contractors with respect to their competitive bidding environments, their own bidding performance and the bidding behaviour of their competitors, thereby enabling contractors to manage risks more effectively and efficiently

Spectral Weights, d-wave Pairing Amplitudes, and Particle-hole Tunneling Asymmetry of a Strongly Correlated Superconductor

The spectral weights (SW's) for adding and removing an electron of the Gutzwiller projected d-wave superconducting (SC) state of the t-J-type models are studied numerically on finite lattices. Restrict to the uniform system but treat exactly the strong correlation between electrons, we show that the product of weights is equal to the pairing amplitude squared, same as in the weakly coupled case. In addition, we derive a rigorous relation of SW with doping in the electron doped system and obtain particle-hole asymmetry of the conductance-proportional quantity within the SC gap energy and, also, the anti-correlation between gap sizes and peak heights observed in tunneling spectroscopy on high Tc cuprates.Comment: 4 Revtex pages and 4 .eps figures. Published versio

Gas Sensing Ionic Liquids on Quartz Crystal Microbalance

Recent advances in “designer solvents” have facilitated the development of ultrasensitive gas sensing ionic liquids (SILs) based on quartz crystal microbalance (QCM) that can real‐time detect and discriminate volatile molecules. The amalgamation of tailored‐made SILs and label‐free QCM resulted in a new class of qualitative and semi‐quantitative gas sensing device, which represents a model system of electronic nose. Because a myriad of human‐made or naturally occurring volatile organic compounds (VOCs) are of great interest in many areas, several functional SILs have been designed to detect gaseous aldehyde, ketone, amine and azide molecules chemoselectively in our laboratory. The versatility of this platform lies in the selective capture of volatile compounds by thin‐coated reactive SILs on QCM at room temperature. Notably, the detection limit of the prototype system can be as low as single‐digit parts‐per‐billion. This chapter briefly introduces some conventional gas sensing approaches and collates recent research results in the integration of SILs and QCM and finally gives an account of the state‐of‐the‐art gas sensing technology

Effect of the translational diffusion mechanism on the low-field NMR spin-lattice relaxation time in the rotating reference frame: calculation of order parameter

Journal ArticleThe effect of the translational-diffusion mechanism on the low-field NMR spin-lattice relaxation time in the rotating reference frame is calculated for simple cubic, body-centered cubic, and face-centered cubic lattices. The results of these calculations suggest a new method for determining the preferred diffusion mechanism. Previously NMR has been able to provide a direct measurement of the activation energy only; a theory has always been needed to determine the jump frequency from the experimentally measured relaxation time. Recently Slichter and Ailion developed a new technique for the study of ultraslow diffusion which is applicable when the mean time r between atomic jumps is less than the spin-lattice relaxation time T\. In their theory, an order parameter p appears in the relationship between the experimentally measured relaxation time and r. This parameter p depends upon the diffusion mechanism and the angle 6, which describes the orientation of the crystal with respect to the external magnetic field. In this paper we have calculated p versus 6 for vacancy diffusion, interstitialcy diffusion, and interstitial diffusion in bcc, fee, and sc lattices for two cases. In the first case, we have assumed that r;, the mean time that an interstitial atom occupies a particular site between jumps, is longer than T2, the spin-spin relaxation time, and we have found that the angular dependence of p is quite different for different mechanisms. In the second case, we have assumed that r% < T% and have found that the angular dependence of p for interstitialcy diffusion differs from the vacancy results by approximately 10% for the three lattices considered. These theoretical results, when combined with experimental measurements of the angular dependence of the low-field relaxation time, provide a method for the direct determination of the mechanism responsible for diffusion in these crystals

Perpendicular Magnetic Anisotropy Materials for Spintronics Applications

Ph.DDOCTOR OF PHILOSOPH

RADAR: Robust AI-Text Detection via Adversarial Learning

Recent advances in large language models (LLMs) and the intensifying popularity of ChatGPT-like applications have blurred the boundary of high-quality text generation between humans and machines. However, in addition to the anticipated revolutionary changes to our technology and society, the difficulty of distinguishing LLM-generated texts (AI-text) from human-generated texts poses new challenges of misuse and fairness, such as fake content generation, plagiarism, and false accusation of innocent writers. While existing works show that current AI-text detectors are not robust to LLM-based paraphrasing, this paper aims to bridge this gap by proposing a new framework called RADAR, which jointly trains a Robust AI-text Detector via Adversarial leaRning. RADAR is based on adversarial training of a paraphraser and a detector. The paraphraser's goal is to generate realistic contents to evade AI-text detection. RADAR uses the feedback from the detector to update the paraphraser, and vice versa. Evaluated with 8 different LLMs (Pythia, Dolly 2.0, Palmyra, Camel, GPT-J, Dolly 1.0, LLaMA, and Vicuna) across 4 datasets, experimental results show that RADAR significantly outperforms existing AI-text detection methods, especially when paraphrasing is in place. We also identify the strong transferability of RADAR from instruction-tuned LLMs to other LLMs, and evaluate the improved capability of RADAR via GPT-3.5.Comment: Preprint. Project page and demos: https://radar.vizhub.a

Neighborhood Failure Localization in All-Optical Networks via Monitoring Trails

Shared protection, such as failure dependent protection (FDP), is well recognized for its outstanding capacity efficiency in all-optical mesh networks, at the expense of lengthy restoration time due to multi-hop signaling mechanisms for failure localization, notification, and device configuration. This paper investigates a novel monitoring trail (m-trail) scenario, called Global Neighborhood Failure Localization (G-NFL), that aims to enable any shared protection scheme, including FDP, for achieving all-optical and ultra-fast failure restoration. We firstly define neighborhood of a node, which is a set of links whose failure states should be known to the node in restoration of the corresponding working lightpaths (W-LPs). By assuming every node can obtain the on-off status of traversing m-trails and W-LPs via lambda monitoring, the proposed G-NFL problem routes a set of m-trails such that each node can localize any failure in its neighborhood. Bound analysis is performed on the minimum bandwidth required for m-trails under the proposed G-NFL problem. Then a simple yet efficient heuristic approach is presented. Extensive simulation is conducted to verify the proposed G-NFL scenario under a number of different definitions of nodal neighborhood which concern the extent of dependency between the monitoring plane and data plane. The effect of reusing the spare capacity by FDP for supporting m-trails is examined. We conclude that the proposed G-NFL scenario enables a general shared protection scheme, toward signaling-free and ultra-fast failure restoration like p-Cycle, while achieving optimal capacity efficiency as FDP

How to Backdoor Diffusion Models?

Diffusion models are state-of-the-art deep learning empowered generative models that are trained based on the principle of learning forward and reverse diffusion processes via progressive noise-addition and denoising. To gain a better understanding of the limitations and potential risks, this paper presents the first study on the robustness of diffusion models against backdoor attacks. Specifically, we propose BadDiffusion, a novel attack framework that engineers compromised diffusion processes during model training for backdoor implantation. At the inference stage, the backdoored diffusion model will behave just like an untampered generator for regular data inputs, while falsely generating some targeted outcome designed by the bad actor upon receiving the implanted trigger signal. Such a critical risk can be dreadful for downstream tasks and applications built upon the problematic model. Our extensive experiments on various backdoor attack settings show that BadDiffusion can consistently lead to compromised diffusion models with high utility and target specificity. Even worse, BadDiffusion can be made cost-effective by simply finetuning a clean pre-trained diffusion model to implant backdoors. We also explore some possible countermeasures for risk mitigation. Our results call attention to potential risks and possible misuse of diffusion models