A Social Media Plan for the Changemakers’ Playground

The purpose of this project was to create a social media plan for the Changemakers’ Playground campaign. The Changemakers’ Playground is a website thought up by our client, Sarah Lange, whereby ordinary individuals doing extraordinary things in their community can be celebrated for the work they are doing. Each person featured on the website, also known as a Changemaker, was interviewed by Sarah and that interview was split into three videos to be posted on the Changemakers’ Playground website throughout the week they are being featured. In short, our task was to create the Facebook, Twitter, and Instagram content to accompany each of these videos in order to maximize visibility and user engagement with the Playground when it eventually launches. The group was also responsible for coming up with at least thirty hashtags to accompany the posts which would again make the posts more visible to the general public and get more people interested in the project

Synthetic θ‐Defensin Antibacterial Peptide as a Highly Efficient Nonviral Vector for Redox‐Responsive miRNA Delivery

Synthetic cationic vectors have shown great promise for nonviral gene delivery. However, their cytotoxicity and low efficiency impose great restrictions on clinic applications. To push through this limitation, humanized peptides or proteins with cationic biocompatibility as well as biodegradation would be an excellent candidate. Herein, for the first time, we describe how an arginine‐rich humanized antimicrobial cyclopeptide, θ‐defensin, can be used as a synthetic cationic vector to load and deliver miRNA into bone mesenchymal stem cells with high efficiency and ultralow cytotoxicity, surpassing the efficiency of the commercial polyethylenimine (25 kD) and Lipofectamine 3000. To note, θ‐defensin can redox‐responsively release the loaded miRNA through a structural change: in extracellular oxidative environment, θ‐defensin has large β‐sheet structures stabilized by three disulfide linkages, and this special structure enables highly efficient delivery of miRNA by passing through cell membranes; in intracellular environment, redox‐responsive disulfide linkages are broken and the tight β‐sheet structures are destroyed, so that the miRNA can be released. Our results suggest that synthetic θ‐defensin peptides are a new class of nonviral gene vectors and this study may also provide a promising strategy to design smart‐responsive gene vectors with high efficiency and minimal toxicity.This study describes how an arginine‐rich humanized antimicrobial cyclopeptide, θ‐defensin, can be used as a synthetic cationic vector to load and deliver miRNA into bone mesenchymal stem cells with high efficiency and low cytotoxicity, surpassing the efficiency of the commercial polyethylenimine (25 kD) and Lipofectamine 3000.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141894/1/adbi201700001.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141894/2/adbi201700001_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141894/3/adbi201700001-sup-0001-S1.pd

MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation

Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical image segmentation tasks. One promising solution is to synthesize realistic data with ground-truth mask annotations. However, no prior studies have explored generating complete 3D volumetric images with masks. In this paper, we present MedGen3D, a deep generative framework that can generate paired 3D medical images and masks. First, we represent the 3D medical data as 2D sequences and propose the Multi-Condition Diffusion Probabilistic Model (MC-DPM) to generate multi-label mask sequences adhering to anatomical geometry. Then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks. Our proposed framework guarantees accurate alignment between synthetic images and segmentation maps. Experiments on 3D thoracic CT and brain MRI datasets show that our synthetic data is both diverse and faithful to the original data, and demonstrate the benefits for downstream segmentation tasks. We anticipate that MedGen3D's ability to synthesize paired 3D medical images and masks will prove valuable in training deep learning models for medical imaging tasks.Comment: Submitted to MICCAI 2023. Project Page: https://krishan999.github.io/MedGen3D

Diffeomorphic Image Registration with Neural Velocity Field

Diffeomorphic image registration, offering smooth transformation and topology preservation, is required in many medical image analysis tasks.Traditional methods impose certain modeling constraints on the space of admissible transformations and use optimization to find the optimal transformation between two images. Specifying the right space of admissible transformations is challenging: the registration quality can be poor if the space is too restrictive, while the optimization can be hard to solve if the space is too general. Recent learning-based methods, utilizing deep neural networks to learn the transformation directly, achieve fast inference, but face challenges in accuracy due to the difficulties in capturing the small local deformations and generalization ability. Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations. A multilayer perceptron (MLP) with sinusoidal activation function is used to represent the continuous velocity field and assigns a velocity vector to every point in space, providing the flexibility of modeling complex deformations as well as the convenience of optimization. Moreover, we propose a cascaded image registration framework (Cas-DNVF) by combining the benefits of both optimization and learning based methods, where a fully convolutional neural network (FCN) is trained to predict the initial deformation, followed by DNVF for further refinement. Experiments on two large-scale 3D MR brain scan datasets demonstrate that our proposed methods significantly outperform the state-of-the-art registration methods.Comment: WACV 202

GHCU, a Molecular Chaperone, Regulates Leaf Curling by Modulating the Distribution of KNGH1 in Cotton

Leaf shape is considered to be one of the most significant agronomic traits in crop breeding. However, the molecular basis underlying leaf morphogenesis in cotton is still largely unknown. In this study, through genetic mapping and molecular investigation using a natural cotton mutant cu with leaves curling upward, the causal gene GHCU is successfully identified as the key regulator of leaf flattening. Knockout of GHCU or its homolog in cotton and tobacco using CRISPR results in abnormal leaf shape. It is further discovered that GHCU facilitates the transport of the HD protein KNOTTED1-like (KNGH1) from the adaxial to the abaxial domain. Loss of GHCU function restricts KNGH1 to the adaxial epidermal region, leading to lower auxin response levels in the adaxial boundary compared to the abaxial. This spatial asymmetry in auxin distribution produces the upward-curled leaf phenotype of the cu mutant. By analysis of single-cell RNA sequencing and spatiotemporal transcriptomic data, auxin biosynthesis genes are confirmed to be expressed asymmetrically in the adaxial-abaxial epidermal cells. Overall, these findings suggest that GHCU plays a crucial role in the regulation of leaf flattening through facilitating cell-to-cell trafficking of KNGH1 and hence influencing the auxin response level

Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction

We present Hybrid-CSR, a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction. Specifically, Hybrid-CSR begins with explicit deformations of template meshes to obtain coarsely reconstructed cortical surfaces, based on which the oriented point clouds are estimated for the subsequent differentiable poisson surface reconstruction. By doing so, our method unifies explicit (oriented point clouds) and implicit (indicator function) cortical surface reconstruction. Compared to explicit representation-based methods, our hybrid approach is more friendly to capture detailed structures, and when compared with implicit representation-based methods, our method can be topology aware because of end-to-end training with a mesh-based deformation module. In order to address topology defects, we propose a new topology correction pipeline that relies on optimization-based diffeomorphic surface registration. Experimental results on three brain datasets show that our approach surpasses existing implicit and explicit cortical surface reconstruction methods in numeric metrics in terms of accuracy, regularity, and consistency

Multi-omics investigation of the resistance mechanisms of pomalidomide in multiple myeloma

BackgroundDespite significant therapeutic advances over the last decade, multiple myeloma remains an incurable disease. Pomalidomide is the third Immunomodulatory drug that is commonly used to treat patients with relapsed/refractory multiple myeloma. However, approximately half of the patients exhibit resistance to pomalidomide treatment. While previous studies have identified Cereblon as a primary target of Immunomodulatory drugs’ anti-myeloma activity, it is crucial to explore additional mechanisms that are currently less understood.MethodsTo comprehensively investigate the mechanisms of drug resistance, we conducted integrated proteomic and metabonomic analyses of 12 plasma samples from multiple myeloma patients who had varying responses to pomalidomide. Differentially expressed proteins and metabolites were screened, and were further analyzed using pathway analysis and functional correlation analysis. Also, we estimated the cellular proportions based on ssGSEA algorithm. To investigate the potential role of glycine in modulating the response of MM cells to pomalidomide, cell viability and apoptosis were analyzed.ResultsOur findings revealed a consistent decrease in the levels of complement components in the pomalidomide-resistant group. Additionally, there were significant differences in the proportion of T follicular helper cell and B cells in the resistant group. Furthermore, glycine levels were significantly decreased in pomalidomide-resistant patients, and exogenous glycine administration increased the sensitivity of MM cell lines to pomalidomide.ConclusionThese results demonstrate distinct molecular changes in the plasma of resistant patients that could be used as potential biomarkers for identifying resistance mechanisms for pomalidomide in multiple myeloma and developing immune-related therapeutic strategies