The Function and Trafficking of Atg8 during Autophagosome Formation.

Eukaryotic cells rely on autophagy to remove excess or damaged organelles and proteins. In this pathway, cytoplasmic materials are delivered to the lysosomes via double-membrane vesicles, the autophagosomes. The formation of autophagosomes, which involves the expansion and deformation of the precursor membrane sac, the phagophore, is catalyzed by the core autophagy machinery proteins at the phagophore assembly site (PAS). Previous studies have gradually discovered the order of assembly of the core autophagy machinery proteins at the PAS. In contrast, we know little about what these proteins do after PAS assembly. In this study, I first focused on how Atg8, one of the core machinery proteins, functions in autophagosome formation and demonstrated that (1) the amount of Atg8 at the PAS controls the size of autophagosomes produced and that (2) each round of autophagosome formation involves the recruitment of Atg8 to the phagophore and the subsequent deconjugation and release of Atg8 from this site. By tracing the trafficking of Atg8 in live cells, I established a temporal dissection of the autophagosome formation process. This allowed the examination of events at late stages of autophagosome formation and led to the further discovery that defects in Atg8 release not only arrest the existing autophagosome formation processes, but also prevent the regeneration of the PAS, which is necessary for sustained autophagosome formation. In addition, the data suggest that the release of Atg8 happens after the departure of Atg9 from the PAS, and that deconjugation of Atg8 is important in maintaining its correct localization. Furthermore, I developed two statistical methods for calculating the sizes of intracellular vesicles from sizes of their sections obtained through transmission electron microscopy. The methods were used to estimate the size of autophagic bodies, which is used in turn to estimate the area density of Atg8 molecules on the phagophore.Ph.D.Molecular, Cellular, and Developmental BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60868/1/zxie_1.pd

Experimental study on natural vibration frequency identification of hydraulic concrete structure using concrete piezoceramic smart module

The identification of structural modal parameter is an important link in the dynamics monitoring and diagnosis for the structural health. The passive monitoring mode of piezoceramic is used to solve the natural vibration frequency identification problem of hydraulic concrete structure. Based on self-made concrete piezoelectric smart module (CPSM), a system is developed to obtain the modal parameters of hydraulic concrete structure. The CPSM is regarded as a sensor to monitor passively the structural natural vibration frequency. The method and process are proposed to identify the natural vibration frequency of hydraulic concrete structure. Based on the physical model and numerical simulation model, the rationality and feasibility of the proposed method are verified

ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection

Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively. Existing CIL either suffers from serious accuracy loss due to catastrophic forgetting, or invades data privacy by revisiting used exemplars. Inspired by linear learning formulations, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data). The absolute memorization is demonstrated in the sense that class-incremental learning using ACIL given present data would give identical results to that from its joint-learning counterpart which consumes both present and historical samples. This equality is theoretically validated. Data privacy is ensured since no historical data are involved during the learning process. Empirical validations demonstrate ACIL's competitive accuracy performance with near-identical results for various incremental task settings (e.g., 5-50 phases). This also allows ACIL to outperform the state-of-the-art methods for large-phase scenarios (e.g., 25 and 50 phases).Comment: published in NeurIPS 202

Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation

Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like GitHub, introduces the risk of inadvertently propagating security vulnerabilities. To effectively mitigate this concern, this paper presents a comprehensive study focused on evaluating and enhancing code LLMs from a software security perspective. We introduce SecuCoGen\footnote{SecuCoGen has been uploaded as supplemental material and will be made publicly available after publication.}, a meticulously curated dataset targeting 21 critical vulnerability types. SecuCoGen comprises 180 samples and serves as the foundation for conducting experiments on three crucial code-related tasks: code generation, code repair and vulnerability classification, with a strong emphasis on security. Our experimental results reveal that existing models often overlook security concerns during code generation, leading to the generation of vulnerable code. To address this, we propose effective approaches to mitigate the security vulnerabilities and enhance the overall robustness of code generated by LLMs. Moreover, our study identifies weaknesses in existing models' ability to repair vulnerable code, even when provided with vulnerability information. Additionally, certain vulnerability types pose challenges for the models, hindering their performance in vulnerability classification. Based on these findings, we believe our study will have a positive impact on the software engineering community, inspiring the development of improved methods for training and utilizing LLMs, thereby leading to safer and more trustworthy model deployment

Magnetic-field-induced splitting of Rydberg Electromagnetically Induced Transparency (EIT) and Autler-Townes (AT) spectra in 87^{87}Rb vapor cell

We theoretically and experimentally investigate the Rydberg electromagnetically induced transparency (EIT) and Autler-Townes (AT) splitting of $^{87}$Rb vapor under the combined influence of a magnetic field and a microwave field. In the presence of static magnetic field, the effect of the microwave field leads to the dressing and splitting of each $m_F$ state, resulting in multiple spectral peaks in the EIT-AT spectrum. A simplified analytical formula was developed to explain the EIT-AT spectrum in a static magnetic field, and the calculations are in excellent agreement with experimental results.We further studied the enhancement of the Rydberg atom microwave electric field sensor performance by making use of the splitting interval between the two maximum absolute $m_F$ states under static magnetic field. The traceable measurement limit of weak electric field by EIT-AT splitting method was extended by an order of magnitude, which is promising for precise microwave electric field measurement.Comment: 12 pages, 4 figure

Phased Geometric Controls of V-Shaped Three-Level System for Zero-field Quantum Sensing

Here we propose and demonstrate a phased geometric control protocol for zero-field double quantum gates in a V-shaped three-level spin system. This method utilizes linearly polarized microwave pulses and exploits the geometric qubit properties to prevent state leakage. By employing specific phased geometric controls, we realize a low-power multi-pulse zero-field sensing technique using single nitrogen-vacancy centers in diamond. Our method offers a novel approach to implement precise double quantum gate operations with an adaptable driving power, making it a valuable tool for zero-field spin-based quantum technology

Microwave electrometry with Rydberg atoms in a vapor cell using microwave amplitude modulation

We have theoretically and experimentally studied the dispersive signal of the Rydberg atomic electromagnetically induced transparency (EIT) - Autler-Townes (AT) splitting spectra obtained using amplitude modulation of the microwave (MW) field. In addition to the two zero-crossing points, the dispersion signal has two positive maxima with an interval defined as the shoulder interval of the dispersion signal $\Delta f_{\text{sho}}$. The relationship of MW field strength $E_{\text{MW}}$ and $\Delta f_{\text{sho}}$ are studied at the MW frequencies of 31.6 GHz, 22.1 GHz, and 9.2 GHz respectively. The results show that $\Delta f_{\text{sho}}$ can be used to character the much weaker $E_{\text{MW}}$ than the interval of two zero-crossing points $\Delta f_{\text{zeros}}$ and the traditional EIT-AT splitting interval $\Delta f_{\text{m}}$, the minimum $E_{\text{MW}}$ measured by $\Delta f_{\text{sho}}$ is about 30 times smaller than that by $\Delta f_{\text{m}}$. As an example, the minimum $E_{\text{MW}}$ at 9.2 GHz that can be characterized by $\Delta f_{\text{sho}}$ is 0.056 mV/cm, which is the minimum value characterized by frequency interval using vapour cell without adding any auxiliary fields. The proposed method can improve the weak limit and sensitivity of $E_{\text{MW}}$ measured by spectral frequency interval, which is important in the direct measurement of weak $E_{\text{MW}}$

Sub-nanotesla Sensitivity at the Nanoscale with a Single Spin

High-sensitivity detection of microscopic magnetic field is essential in many fields. Good sensitivity and high spatial resolution are mutually contradictory in measurement, which is quantified by the energy resolution limit (ERL). Here we report that a sensitivity of 0.5 ${\bf{nT/\sqrt{Hz}}}$ at the nanoscale is achieved experimentally by using nitrogen-vacancy defects in diamond with depths of tens of nanometers. The achieved sensitivity is substantially enhanced by integrating with multiple quantum techniques, including real-time-feedback initialization, dynamical decoupling with shaped pulses, repetitive readout via quantum logic. Our magnetic sensors will shed new light on searching new physics beyond the standard model, investigating microscopic magnetic phenomena in condensed matters, and detection of life activities at the sub-cellular scale.Comment: 27 pages, 4 figure