Investigate the Potential of Small Molecule Drug Candidates for the Inhibition of p53 Mutant Aggregation and Cancer Cell Proliferation

The master’s thesis study focuses on the identification of novel small molecule drug candidates for inhibiting cancer causing p53 mutant peptide aggregation and tumor growth. p53 protein is a tumor suppressor protein, and controls cellular function and unwanted cell proliferation.When p53 is mutated it loses its function. Mutations of p53 are present in almost about 50-70% of all cancers. In a recent study, it has been reported that the p53 mutations cause aggregation and subsequent loss of p53 function, negative dominance and cell toxicity leading to advanced cancers. Further, p53 mutant aggregation has been observed in several cancers. Hence, there is growing interest in finding therapies for p53 mutant aggregation associated cancer. The objective of the thesis study include studying the inhibitory effect of small molecule drugs on p53 aggregation in vitro, their inhibitory potential on p53 mutant cancer cells proliferation in vitro, and finally an anoformulation of p53-antiaggregation drug candidates to treat p53 aggregation associated cancer with increased therapeutic efficacy. Characterization tools used for this study include biochemical assays, transmission electron microscopy, confocal microscopy, atomic force microscopy, dynamic light scattering, and cellular assays. The results of the thesis study show potential of small molecule drugs for treating cancer due to p53 aggregation.Master of Science in EngineeringMechanical Engineering, College of Engineering and Computer ScienceCollege of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/138103/1/Investigate the Potential of Small Molecule Drug Candidates for the Inhibition of p53 Mutant Aggregation and Cancer Cell Proliferation.pdfDescription of Investigate the Potential of Small Molecule Drug Candidates for the Inhibition of p53 Mutant Aggregation and Cancer Cell Proliferation.pdf : Thesi

Electrochemical Insertion/extraction of Lithium in Multiwall Carbon Nanotube/Sb and SnSb₀.₅ Nanocomposites

Multiwall carbon nanotubes (CNTs) were synthesized by catalytic chemical vapor deposition of acetylene and used as templates to prepare CNT-Sb and CNT-SnSb₀.₅ nanocomposites via the chemical reduction of SnCl₂ and SbCl₃ precursors. SEM and TEM imagings show that the Sb and SnSb₀.₅ particles were uniformly dispersed in the CNT web and on the outside surface of CNTs. These CNT-metal composites are active anode materials for lithium ion batteries, showing improved cyclability compared to unsupported Sb and SnSb particles; and higher reversible specific capacities than CNTs. The improvement in cyclability may be attributed to the nanoscale dimensions of the metal particles and CNT’s role as a buffer in containing the mechanical stress arising from the volume changes in electrochemical lithium insertion and extraction reactions.Singapore-MIT Alliance (SMA

Contrastive Learning MRI Reconstruction

Purpose: We propose a novel contrastive learning latent space representation for MRI datasets with partially acquired scans. We show that this latent space can be utilized for accelerated MR image reconstruction. Theory and Methods: Our novel framework, referred to as COLADA (stands for Contrastive Learning for highly accelerated MR image reconstruction), maximizes the mutual information between differently accelerated images of an MRI scan by using self-supervised contrastive learning. In other words, it attempts to "pull" the latent representations of the same scan together and "push" the latent representations of other scans away. The generated MRI latent space is subsequently utilized for MR image reconstruction and the performance was assessed in comparison to several baseline deep learning reconstruction methods. Furthermore, the quality of the proposed latent space representation was analyzed using Alignment and Uniformity. Results: COLADA comprehensively outperformed other reconstruction methods with robustness to variations in undersampling patterns, pathological abnormalities, and noise in k-space during inference. COLADA proved the high quality of reconstruction on unseen data with minimal fine-tuning. The analysis of representation quality suggests that the contrastive features produced by COLADA are optimally distributed in latent space. Conclusion: To the best of our knowledge, this is the first attempt to utilize contrastive learning on differently accelerated images for MR image reconstruction. The proposed latent space representation has practical usage due to a large number of existing partially sampled datasets. This implies the possibility of exploring self-supervised contrastive learning further to enhance the latent space of MRI for image reconstruction

Physical Layer Security in Near-Field Communications: What Will Be Changed?

A near-field secure transmission framework is proposed. Employing the hybrid beamforming architecture, a base station (BS) transmits the confidential information to a legitimate user (U) against an eavesdropper (E) in the near field. A two-stage algorithm is proposed to maximize the near-field secrecy capacity. Based on the fully-digital beamformers obtained in the first stage, the optimal analog beamformers and baseband digital beamformers can be alternatingly derived in the closed-form expressions in the second stage. Numerical results demonstrate that in contrast to the far-field secure communication relying on the angular disparity, the near-filed secure communication mainly relies on the distance disparity between U and E.Comment: 5 page

Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation

As intensities of MRI volumes are inconsistent across institutes, it is essential to extract universal features of multi-modal MRIs to precisely segment brain tumors. In this concept, we propose a volumetric vision transformer that follows two windowing strategies in attention for extracting fine features and local distributional smoothness (LDS) during model training inspired by virtual adversarial training (VAT) to make the model robust. We trained and evaluated network architecture on the FeTS Challenge 2022 dataset. Our performance on the online validation dataset is as follows: Dice Similarity Score of 81.71%, 91.38% and 85.40%; Hausdorff Distance (95%) of 14.81 mm, 3.93 mm, 11.18 mm for the enhancing tumor, whole tumor, and tumor core, respectively. Overall, the experimental results verify our method's effectiveness by yielding better performance in segmentation accuracy for each tumor sub-region. Our code implementation is publicly available : https://github.com/himashi92/vizviva_fets_202

A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode local and global cues, while the decoder employs a parallel self and cross attention formulation to capture fine details for boundary refinement. Empirically, we show that the proposed design choices result in a computationally efficient model, with competitive and promising results on the Medical Segmentation Decathlon (MSD) brain tumor segmentation (BraTS) Task. We further show that the representations learned by our model are robust against data corruptions. \href{https://github.com/himashi92/VT-UNet}{Our code implementation is publicly available}

The Explicit Identities for Spectral Norms of Circulant-Type Matrices Involving Binomial Coefficients and Harmonic Numbers

The explicit formulae of spectral norms for circulant-type matrices are investigated; the matrices are circulant matrix, skew-circulant matrix, and g-circulant matrix, respectively. The entries are products of binomial coefficients with harmonic numbers. Explicit identities for these spectral norms are obtained. Employing these approaches, some numerical tests are listed to verify the results