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Open Access Publications from the University of California

Scholarly Works (11 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2012)

The possibility of building a computer that takes advantage of the most subtle nature of quantum physics has been driving a lot of research in atomic and solid state physics for some time. It is still not clear what physical system or systems can be used for this purpose. One possibility that has been attracting significant attention from researchers is to use the spin state of an electron confined in a semiconductor quantum dot. The electron spin is magnetic in nature, so it naturally is well isolated from electrical fluctuations that can a loss of quantum coherence. It can also be manipulated electrically, by taking advantage of the exchange interaction. In this work we describe several experiments we have done to study the electron spin properties of lateral quantum dots. We have developed lateral quantum dot devices based on the silicon metal-oxide-semiconductor transistor, and studied the physics of electrons confined in these quantum dots. We measured the electron spin excited state lifetime, which was found to be as long as 30 ms at the lowest magnetic fields that we could measure. We fabricated and characterized a silicon double quantum dot. Using this double quantum dot design, we fabricated devices which combined a silicon double quantum dot with a superconducting microwave resonator. The microwave resonator was found to be sensitive to two-dimensional electrons in the transistor channel, which we measured and characterized. We developed a new method for extracting information from random telegraph signals, which are produced when we observe thermal fluctuations of electrons in quantum dots. The new statistical method, based on the hidden Markov model, allows us to detect spin-dependent effects in such fluctuations even though we are not able to directly observe the electron spin. We use this analysis technique on data from two experiments involving gallium arsenide quantum dots and use it to measure spin-dependent tunneling rates. Our results advance the understanding of electron spin physics in lateral quantum dots, in silicon and in gallium arsenide.

    Cover page: Lateral Quantum Dots for Quantum Information Processing
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2017)

    Purpose

    To investigate two-dimensional (2D) and three-dimensional (3D) ultrashort echo time (UTE) and 3D magnetization-prepared rapid gradient-echo (MP-RAGE) sequences for the imaging of iron-oxide nanoparticles (IONP).

    Methods

    The phantoms were composed of tubes filled with different IONP concentrations ranging from 2 to 45 mM. The tubes were fixed in an agarose gel phantom (0.9% by weight). Morphological imaging was performed with 3D MP-RAGE, 2D UTE, 2D adiabatic inversion recovery-prepared UTE (2D IR-UTE), 3D UTE with Cones trajectory (3D Cones), and 3D IR-Cones sequences. Quantitative assessment of IONP concentration was performed using R2*(1/T2*) and R1 (1/T1 ) measurements using a 3 Tesla (T) scanner.

    Results

    The 3D MP-RAGE sequence provides high-contrast images of IONP with concentration up to 7.5 mM. Higher IONP concentration up to 37.5 mM can be detected with the UTE sequences, with the highest IONP contrast provided by the 3D IR-Cones sequence. A linear relationship was observed between R2* and IONP concentration up to ∼45 mM, and between R1 and IONP concentration up to ∼30 mM.

    Conclusion

    The clinical 3D MP-RAGE sequence can be used to assess lower IONP concentration up to 7.5 mM. The UTE sequences can be used to assess higher IONP concentration up to 45 mM. Magn Reson Med 78:226-232, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
      Cover page: Imaging and quantification of iron‐oxide nanoparticles (IONP) using MP‐RAGE and UTE based sequences
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2010)
      Heart failure is a leading cause of death in the United States, with around 5 million Americans currently suffering from congestive heart failure. The WANDA B. wireless health technology leverages sensor technology and wireless communication to monitor heart failure patient activity and to provide tailored guidance. Patients who have cardiovascular system disorders can measure their weight, blood pressure, activity levels, and other vital signs in a real-time automated fashion. The system was developed in conjunction with the UCLA Nursing School and the UCLA Wireless Health Institute for use on actual patients. It is currently in use with real patients in a clinical trial. © 2010 IEEE.
        Cover page: WANDA B.: Weight and Activity with Blood Pressure Monitoring System for Heart Failure Patients
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2024)
        UNLABELLED: Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence. PURPOSE: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. METHODS: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. RESULTS: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001). CONCLUSIONS: The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.
          Cover page: A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure.
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2024)
          Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on-skin sensor to reduce electrode-skin contact impedance by increasing double-layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention-based deep learning algorithm is introduced to differentiate impedance signals associated with early-stage NAFLD in high-fat-diet-fed low-density lipoprotein receptor knockout (Ldlr-/-) mice compared to healthy controls. The integration of an adhesive, soft on-skin sensor with low electrode-skin contact impedance and the attention-based deep learning algorithm significantly enhances the detection accuracy for early-stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non-invasive approach for early-stage NAFLD detection and display a strategy for improved early detection through on-skin electronics and deep learning.
            Cover page: Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.
            • Article
            • Peer Reviewed
            UC Irvine Previously Published Works (2021)
            Dysregulated mTORC1 signaling alters a wide range of cellular processes, contributing to metabolic disorders and cancer. Defining the molecular details of downstream effectors is thus critical for uncovering selective therapeutic targets. We report that mTORC1 and its downstream kinase S6K enhance eIF4A/4B-mediated translation of Wilms' tumor 1-associated protein (WTAP), an adaptor for the N6-methyladenosine (m6A) RNA methyltransferase complex. This regulation is mediated by 5' UTR of WTAP mRNA that is targeted by eIF4A/4B. Single-nucleotide-resolution m6A mapping revealed that MAX dimerization protein 2 (MXD2) mRNA contains m6A, and increased m6A modification enhances its degradation. WTAP induces cMyc-MAX association by suppressing MXD2 expression, which promotes cMyc transcriptional activity and proliferation of mTORC1-activated cancer cells. These results elucidate a mechanism whereby mTORC1 stimulates oncogenic signaling via m6A RNA modification and illuminates the WTAP-MXD2-cMyc axis as a potential therapeutic target for mTORC1-driven cancers.
              Cover page: mTORC1 promotes cell growth via m6A-dependent mRNA degradation
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2020)
              Purpose: Proximal femur image analyses based on quantitative computed tomography (QCT) provide a method to quantify the bone density and evaluate osteoporosis and risk of fracture. We aim to develop a deep-learning-based method for automatic proximal femur segmentation. Methods and Materials: We developed a 3D image segmentation method based on V-Net, an end-to-end fully convolutional neural network (CNN), to extract the proximal femur QCT images automatically. The proposed V-net methodology adopts a compound loss function, which includes a Dice loss and a L2 regularizer. We performed experiments to evaluate the effectiveness of the proposed segmentation method. In the experiments, a QCT dataset which included 397 QCT subjects was used. For the QCT image of each subject, the ground truth for the proximal femur was delineated by a well-trained scientist. During the experiments for the entire cohort then for male and female subjects separately, 90% of the subjects were used in 10-fold cross-validation for training and internal validation, and to select the optimal parameters of the proposed models; the rest of the subjects were used to evaluate the performance of models. Results: Visual comparison demonstrated high agreement between the model prediction and ground truth contours of the proximal femur portion of the QCT images. In the entire cohort, the proposed model achieved a Dice score of 0.9815, a sensitivity of 0.9852 and a specificity of 0.9992. In addition, an R2 score of 0.9956 (p<0.001) was obtained when comparing the volumes measured by our model prediction with the ground truth. Conclusion: This method shows a great promise for clinical application to QCT and QCT-based finite element analysis of the proximal femur for evaluating osteoporosis and hip fracture risk.
                Cover page: A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2021)
                Mechanistic Target of Rapamycin Complex 1 (mTORC1) is a central regulator of cell growth and metabolism that senses and integrates nutritional and environmental cues with cellular responses. Recent studies have revealed critical roles of mTORC1 in RNA biogenesis and processing. Here, we find that the m6A methyltransferase complex (MTC) is a downstream effector of mTORC1 during autophagy in Drosophila and human cells. Furthermore, we show that the Chaperonin Containing Tailless complex polypeptide 1 (CCT) complex, which facilitates protein folding, acts as a link between mTORC1 and MTC. The mTORC1 activates the chaperonin CCT complex to stabilize MTC, thereby increasing m6A levels on the messenger RNAs encoding autophagy-related genes, leading to their degradation and suppression of autophagy. Altogether, our study reveals an evolutionarily conserved mechanism linking mTORC1 signaling with m6A RNA methylation and demonstrates their roles in suppressing autophagy.
                  Cover page: mTORC1-chaperonin CCT signaling regulates m6A RNA methylation to suppress autophagy
                  • Article
                  • Peer Reviewed
                  UC Irvine Previously Published Works (2023)
                  We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance.
                    • Article
                    • Peer Reviewed
                    UC Irvine Previously Published Works (2023)

                    Background

                    Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion.

                    Results

                    We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively.

                    Conclusion

                    The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.
                      Cover page: Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load
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