MECHANISMS OF ORIENTED CELL DIVISION AND THEIR ROLES IN TISSUE DEVELOPMENT

Properly executed cell division is crucial to development, maintenance, and longevity of multicellular organisms. Defects in both symmetric and asymmetric divisions can lead to improper developmental patterning, as well as genomic instability, disruption of tissue homeostasis, and cancer. Our research focuses on how regulators orchestrate proper cell divisions. Mushroom Body Defect (Mud) is one such regulator, and here we describe how Mud is regulated via the Hippo signaling pathway kinase Warts (Wts), showing Wts phosphorylates Mud to enhance interaction with the polarity protein Partner of Inscuteable, promoting spindle orientation activity. We next focus on another regulator, Shortstop (Shot), describing a role for Shot in cell divisions, with both tissue culture and in vivoDrosophilaepithelial models showing spindle assembly, spindle misalignment, and chromosome migration defects in Shot knockdowns (KDs). These activities are mediated not only through traditional Shot roles in stabilization of spindle MTs through crosslinks to actin, but also through direct interaction of Shot to dynein activator subunit actin-related protein 1 (Arp1). We hypothesize Shot interaction with Arp1 functions to crosslink it to spindle MTs, facilitating MT motor protein Dynein activity, promoting its activities in cell division. Live cell imaging experiments show defects in cell division timing under Shot KD conditions, implicating involvement of the spindle assembly checkpoint (SAC). Inhibition of SAC components under Shot KD conditions leading to timing rescue. Shot loss in vivoleads increases in apoptosis, in line with previous findings linking mitotic regulators to cell death. Previous studies implicated induction of the jun-N-kinase (JNK) apoptotic pathway under spindle regulator KD, but Shot KD apoptosis likely does not utilize JNK. When Shot KD-induced apoptosis is inhibited, tumorigenic-like conditions result, underscoring the importance of Shot as a key component in development and maintenance of multicellular organisms. Shot KD-induced apoptosis is likely mediated via p53 and the DNA damage response (DDR), with DNA double strand breaks occurring in Shot KD, and additionally enhanced when coupled to SAC inhibition. Finally, we utilize mRNA sequencing (RNAseq) to describe Shot KD-induced genes involved in DDR, highlighting a distinct mechanism to mitigate loss of a key oriented cell division regulator

Self Super-Resolution for Magnetic Resonance Images using Deep Networks

High resolution magnetic resonance~(MR) imaging~(MRI) is desirable in many clinical applications, however, there is a trade-off between resolution, speed of acquisition, and noise. It is common for MR images to have worse through-plane resolution~(slice thickness) than in-plane resolution. In these MRI images, high frequency information in the through-plane direction is not acquired, and cannot be resolved through interpolation. To address this issue, super-resolution methods have been developed to enhance spatial resolution. As an ill-posed problem, state-of-the-art super-resolution methods rely on the presence of external/training atlases to learn the transform from low resolution~(LR) images to high resolution~(HR) images. For several reasons, such HR atlas images are often not available for MRI sequences. This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image. We use a blurred version of the input image to create training data for a state-of-the-art super-resolution deep network. The trained network is applied to the original input image to estimate the HR image. Our SSR result shows a significant improvement on through-plane resolution compared to competing SSR methods.Comment: Accepted by IEEE International Symposium on Biomedical Imaging (ISBI) 201

Synthesis-Based Harmonization of Multi-Contrast Structural MRI

Magnetic resonance imaging (MRI) is a flexible, noninvasive medical imaging modality that uses magnetic fields and radiofrequency (RF) pulses to produce images. MRI is especially useful in diagnosing and monitoring disorders of the central nervous system such as multiple sclerosis (MS). The flexible design of the MRI system allows for the collection of multiple images with different acquisition parameters in a single scanning session. This flexibility also poses challenges when pooling data collected on multiple scanners or at different sites. Since MRI does not have consistent standards that regulate image acquisition, differences in acquisition lead to variability in images that can cause problems in analysis. This problem sets the stage for harmonization. This dissertation describes developments in harmonization strategies for structural MRI of the brain. These strategies allow us to create similar harmonized images from varying source images. Harmonized images can then be used in automated analysis pipelines where image variability can cause inconsistent results. In this work, we make a number of contributions to research in harmonization of MRI. In our first contribution, we acquired a cross-domain dataset to provide training and validation data for our harmonization methods. These data were acquired on two different scanners with different acquisition protocols in a short time frame, providing examples of the same subjects under two different acquisition environments. Since the imaged object is the same between the two, this can be used as training and validation data in harmonization experiments. In our second contribution, we used this dataset to develop a supervised method of harmonization, called DeepHarmony, which uses state-of-the-art deep learning architecture and training strategies to provide improved image harmonization. This method provides a baseline for image harmonization, but the requirement for cross-domain training data is a major limitation. In our third contribution, we proposed an unsupervised harmonization framework. We used multi-contrast MRI images from the same scanning session to encourage a disentangled latent representation and we demonstrated that this regularization was able to generate disentanglement and allow for harmonization. In our final contribution, we extended our unsupervised work for a more diverse clinical trial dataset, which included T2-FLAIR and PD-weighted images. In this more complex dataset, we included a new adversarial loss to encourage consistency in the anatomy space and a distribution loss to impose a well-defined distribution on the acquisition space

Seasonal Resource Selection by Introduced Mountain Goats in the Southwest Greater Yellowstone Area

Mountain ungulates, although regarded as iconic and charismatic wildlife species, are the least studied and understood large mammals in the Greater Yellowstone Area (GYA). Mountain goats (Oreamnos americanus) are considered non-native in the GYA according to reviews of archeological, paleontological, and historical records, and have been steadily expanding their range since their initial introduction in the 1940s.  Because of the general propensity of mountain goats to inhabit high elevation, mountainous terrain, there is significant potential for range overlap with native bighorn sheep (Ovis canadensis) and the possibility that competition and disease transfer will be detrimental to sympatric bighorn populations.  I will broadly discuss mountain goat seasonal resource selection modeled from 15 (11 females and 4 males) allopatric mountain goats representing the sole established population in the southwest GYA.  These efforts produce the first spatial predictions of seasonal habitat use by mountain goats in the GYA using GPS data, and provide regional managers with important insights regarding the current and future distribution of mountain goats.  Of particular interest are areas where mountain goats are in the early stages of colonization, such as Grand Teton National Park.  Building seasonal resource selection models for mountain goats in the GYA is the first step needed to better understand their biological needs, ecological role, and potential to negatively impact native communities and species

Relating multi-sequence longitudinal intensity profiles and clinical covariates in new multiple sclerosis lesions

Structural magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients. The formation of these lesions is a complex process involving inflammation, tissue damage, and tissue repair, all of which are visible on MRI. Here we characterize the lesion formation process on longitudinal, multi-sequence structural MRI from 34 MS patients and relate the longitudinal changes we observe within lesions to therapeutic interventions. In this article, we first outline a pipeline to extract voxel level, multi-sequence longitudinal profiles from four MRI sequences within lesion tissue. We then propose two models to relate clinical covariates to the longitudinal profiles. The first model is a principal component analysis (PCA) regression model, which collapses the information from all four profiles into a scalar value. We find that the score on the first PC identifies areas of slow, long-term intensity changes within the lesion at a voxel level, as validated by two experienced clinicians, a neuroradiologist and a neurologist. On a quality scale of 1 to 4 (4 being the highest) the neuroradiologist gave the score on the first PC a median rating of 4 (95% CI: [4,4]), and the neurologist gave it a median rating of 3 (95% CI: [3,3]). In the PCA regression model, we find that treatment with disease modifying therapies (p-value < 0.01), steroids (p-value < 0.01), and being closer to the boundary of abnormal signal intensity (p-value < 0.01) are associated with a return of a voxel to intensity values closer to that of normal-appearing tissue. The second model is a function-on-scalar regression, which allows for assessment of the individual time points at which the covariates are associated with the profiles. In the function-on-scalar regression both age and distance to the boundary were found to have a statistically significant association with the profiles

AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution

Anisotropic low-resolution (LR) magnetic resonance (MR) images are fast to obtain but hinder automated processing. We propose to use denoising diffusion probabilistic models (DDPMs) to super-resolve these 2D-acquired LR MR slices. This paper introduces AniRes2D, a novel approach combining DDPM with a residual prediction for 2D super-resolution (SR). Results demonstrate that AniRes2D outperforms several other DDPM-based models in quantitative metrics, visual quality, and out-of-domain evaluation. We use a trained AniRes2D to super-resolve 3D volumes slice by slice, where comparative quantitative results and reduced skull aliasing are achieved compared to a recent state-of-the-art self-supervised 3D super-resolution method. Furthermore, we explored the use of noise conditioning augmentation (NCA) as an alternative augmentation technique for DDPM-based SR models, but it was found to reduce performance. Our findings contribute valuable insights to the application of DDPMs for SR of anisotropic MR images.Comment: Accepted for presentation at SPIE Medical Imaging 2024, Clinical and Biomedical Imagin

A randomised controlled trial of a care home rehabilitation service to reduce long-term institutionalisation for elderly people

Objectives: to evaluate the effect of a care home rehabilitation service on institutionalisation, health outcomes and service use. Design: randomised controlled trial, stratified by Barthel ADL index, social service sector and whether living alone. The intervention was a rehabilitation service based in Social Services old people's homes in Nottingham, UK. The control group received usual health and social care. Participants: 165 elderly and disabled hospitalised patients who wished to go home but were at high risk of institutionalisation (81 intervention, 84 control). Main outcome measures: institutionalisation rates, Barthel ADL index, Nottingham Extended ADL score, General Health Questionnaire (12 item version) at 3 and 12 months, Health and Social Service resource use. Results: the number of participants institutionalised was similar at 3 months (relative risk 1.04, 95% confidence intervals 0.65–1.65) and 12 months (relative risk 1.23, 95% confidence intervals 0.75–2.02). Barthel ADL Index, Nottingham Extended ADL score and General Health Questionnaire scores were similar at 3 and 12 months. The intervention group spent significantly fewer days in hospital over 3 and 12 months (mean reduction 12.1 and 27.6 days respectively, P < 0.01), but spent a mean of 36 days in a care home rehabilitation service facility. Conclusions: this service did not reduce institutionalisation, but diverted patients from the hospital to social services sector without major effects on activity levels or well-being

Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite their achievements, these algorithms may struggle to extend their performance across various sites or scanners, leading to domain generalization errors. While few-shot or one-shot domain adaptation emerges as a potential solution to mitigate generalization errors, its efficacy might be hindered by the scarcity of labeled data in the target domain. This paper seeks to tackle this challenge by integrating one-shot adaptation data with harmonized training data that incorporates labels. Our approach involves synthesizing new training data with a contrast akin to that of the test domain, a process we refer to as "contrast harmonization" in MRI. Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation. Notably, domain adaptation using exclusively harmonized training data achieved comparable or even superior performance compared to one-shot adaptation. Moreover, all adaptations required only minimal fine-tuning, ranging from 2 to 5 epochs for convergence