Integration of Polyimide Flexible PCB Wings in Northeastern Aerobat

The principal aim of this Master's thesis is to propel the optimization of the membrane wing structure of the Northeastern Aerobat through origami techniques and enhancing its capacity for secure hovering within confined spaces. Bio-inspired drones offer distinctive capabilities that pave the way for innovative applications, encompassing wildlife monitoring, precision agriculture, search and rescue operations, as well as the augmentation of residential safety. The evolved noise-reduction mechanisms of birds and insects prove advantageous for drones utilized in tasks like surveillance and wildlife observation, ensuring operation devoid of disturbances. Traditional flying drones equipped with rotary or fixed wings encounter notable constraints when navigating narrow pathways. While rotary and fixed-wing systems are conventionally harnessed for surveillance and reconnaissance, the integration of onboard sensor suites within micro aerial vehicles (MAVs) has garnered interest in vigilantly monitoring hazardous scenarios in residential settings. Notwithstanding the agility and commendable fault tolerance exhibited by systems such as quadrotors in demanding conditions, their inflexible body structures impede collision tolerance, necessitating operational spaces free of collisions. Recent years have witnessed an upsurge in integrating soft and pliable materials into the design of such systems; however, the pursuit of aerodynamic efficiency curtails the utilization of excessively flexible materials for rotor blades or propellers. This thesis introduces a design that integrates polyimide flexible PCBs into the wings of the Aerobat and employs guard design incorporating feedback-driven stabilizers, enabling stable hovering flights within Northeastern's Robotics-Inspired Study and Experimentation (RISE) cage.Comment: 42 pages,20 figure

Self-Supervised Time-to-Event Modeling with Structured Medical Records

Time-to-event (TTE) models are used in medicine and other fields for estimating the probability distribution of the time until a specific event occurs. TTE models provide many advantages over classification using fixed time horizons, including naturally handling censored observations, but require more parameters and are challenging to train in settings with limited labeled data. Existing approaches, e.g. proportional hazards or accelerated failure time, employ distributional assumptions to reduce parameters but are vulnerable to model misspecification. In this work, we address these challenges with MOTOR (Many Outcome Time Oriented Representations), a self-supervised model that leverages temporal structure found in collections of timestamped events in electronic health records (EHR) and health insurance claims. MOTOR uses a TTE pretraining objective that predicts the probability distribution of times when events occur, making it well-suited to transfer learning for medical prediction tasks. Having pretrained on EHR and claims data of up to 55M patient records (9B clinical events), we evaluate performance after finetuning for 19 tasks across two datasets. Task-specific models built using MOTOR improve time-dependent C statistics by 4.6% over state-of-the-art while greatly improving sample efficiency, achieving comparable performance to existing methods using only 5% of available task data

Clinical Utility Gains from Incorporating Comorbidity and Geographic Location Information into Risk Estimation Equations for Atherosclerotic Cardiovascular Disease

Objective: There are several efforts to re-learn the 2013 ACC/AHA pooled cohort equations (PCE) for patients with specific comorbidities and geographic locations. With over 363 customized risk models in the literature, we aim to evaluate such revised models to determine if the performance improvements translate to gains in clinical utility. Methods: We re-train a baseline PCE using the ACC/AHA PCE variables and revise it to incorporate subject-level geographic location and comorbidity information. We apply fixed effects, random effects, and extreme gradient boosting models to handle the correlation and heterogeneity induced by locations. Models are trained using 2,464,522 claims records from Optum Clinformatics Data Mart and validated in the hold-out set (N=1,056,224). We evaluate models' performance overall and across subgroups defined by the presence or absence of chronic kidney disease (CKD) or rheumatoid arthritis (RA) and geographic locations. We evaluate models' expected net benefit using decision curve analysis and models' statistical properties using several discrimination and calibration metrics. Results: The baseline PCE is miscalibrated overall, in patients with CKD or RA, and locations with small populations. Our revised models improved both the overall (GND P-value=0.41) and subgroup calibration but only enhanced net benefit in the underrepresented subgroups. The gains are larger in the subgroups with comorbidities and heterogeneous across geographic locations. Conclusions: Revising the PCE with comorbidity and location information significantly enhanced models' calibration; however, such improvements do not necessarily translate to clinical gains. Thus, we recommend future works to quantify the consequences from using risk calculators to guide clinical decisions

Shifted Diffusion for Text-to-image Generation

We present Corgi, a novel method for text-to-image generation. Corgi is based on our proposed shifted diffusion model, which achieves better image embedding generation from input text. Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new initialization distribution and a new transition step of the diffusion. Compared to the strong DALL-E 2 baseline, our method performs better in generating image embedding from the text in terms of both efficiency and effectiveness, resulting in better text-to-image generation. Extensive large-scale experiments are conducted and evaluated in terms of both quantitative measures and human evaluation, indicating a stronger generation ability of our method compared to existing ones. Furthermore, our model enables semi-supervised and language-free training for text-to-image generation, where only part or none of the images in the training dataset have an associated caption. Trained with only 1.7% of the images being captioned, our semi-supervised model obtains FID results comparable to DALL-E 2 on zero-shot text-to-image generation evaluated on MS-COCO. Corgi also achieves new state-of-the-art results across different datasets on downstream language-free text-to-image generation tasks, outperforming the previous method, Lafite, by a large margin

SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation

High-resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting non-local information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a Superpixel High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in high-resolution SAR mode. Based on the concept of superpixel techniques, we initially combine non-convex and non-local total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the Deep Unfolded Network is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from raw echo data, producing accurate imaging results

Nrf2 deletion causes “benign” simple steatosis to develop into nonalcoholic steatohepatitis in mice fed a high-fat diet

BACKGROUND: Nonalcoholic fatty liver disease begins with the aberrant accumulation of triglyceride in the liver. Its spectrum includes the earliest stage of hepatic simple steatosis (SS), nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Generally, hepatic SS is often self-limited; however 10%-30% of patients with hepatic SS progress to NASH. The cause(s) of the transition from SS to NASH are unclear. We aimed to test the contribution of nuclear erythroid 2-related factor 2 (Nrf2) on the progression of “benign” SS to NASH in mice fed a high fat diet. In doing so, we discovered the influence of fatty acid in that progression. METHOD: The involvement of Nrf2 in defending against the development of NASH was studied in an experimental model induced by a high-fat diet. Wild-type and Nrf2-null mice were fed the diet. Their specimens were analyzed for pathology as well as for fatty acid content and ratios. RESULT: In feeding the high-fat diet to the Wild-type and the Nrf2-null mice, the Wild-type mice increased hepatic fat deposition without inflammation or fibrosis (i.e., simple steatosis), while the Nrf2-null mice had significantly more hepatic steatosis and substantial inflammation, (i.e., nonalcoholic steatohepatitis). In addition, as a result of the high-fat diet, SFA (C20: 0, C22: 0) and MUFA (C18: 1, C20: 1) content in Nrf2-null mice were significantly higher than in Wild-type mice. In the Nrf2-null mice the PUFA/TFA ratio decreased; conversely, the MUFA/TFA ratio increased. CONCLUSION: The deletion of Nrf2 causes “benign” SS to develop into NASH in mice fed with a high-fat diet, through prompt fatty acid accumulation and disruption of hepatic fatty acid composition in the liver

Lactobacillus reuteri in digestive system diseases: focus on clinical trials and mechanisms

ObjectivesDigestive system diseases have evolved into a growing global burden without sufficient therapeutic measures. Lactobacillus reuteri (L. reuteri) is considered as a new potential economical therapy for its probiotic effects in the gastrointestinal system. We have provided an overview of the researches supporting various L. reuteri strains’ application in treating common digestive system diseases, including infantile colic, diarrhea, constipation, functional abdominal pain, Helicobacter pylori infection, inflammatory bowel disease, diverticulitis, colorectal cancer and liver diseases.MethodsThe summarized literature in this review was derived from databases including PubMed, Web of Science, and Google Scholar.ResultsThe therapeutic effects of L. reuteri in digestive system diseases may depend on various direct and indirect mechanisms, including metabolite production as well as modulation of the intestinal microbiome, preservation of the gut barrier function, and regulation of the host immune system. These actions are largely strain-specific and depend on the activation or inhibition of various certain signal pathways. It is well evidenced that L. reuteri can be effective both as a prophylactic measure and as a preferred therapy for infantile colic, and it can also be recommended as an adjuvant strategy to diarrhea, constipation, Helicobacter pylori infection in therapeutic settings. While preclinical studies have shown the probiotic potential of L. reuteri in the management of functional abdominal pain, inflammatory bowel disease, diverticulitis, colorectal cancer and liver diseases, its application in these disease settings still needs further study.ConclusionThis review focuses on the probiotic effects of L. reuteri on gut homeostasis via certain signaling pathways, and emphasizes the importance of these probiotics as a prospective treatment against several digestive system diseases

The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs

The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. We review over 80 foundation models trained on non-imaging EMR data (i.e. clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g. MIMIC-III) or broad, public biomedical corpora (e.g. PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. In light of these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.Comment: Reformatted figures, updated contribution

Beyond substrates : strain engineering of ferroelectric membranes

Strain engineering in perovskite oxides provides for dramatic control over material structure, phase, and properties, but is restricted by the discrete strain states produced by available high-quality substrates. Here, using the ferroelectric BaTiO, production of precisely strain-engineered, substrate-released nanoscale membranes is demonstrated via an epitaxial lift-off process that allows the high crystalline quality of films grown on substrates to be replicated. In turn, fine structural tuning is achieved using interlayer stress in symmetric trilayer oxide-metal/ferroelectric/oxide-metal structures fabricated from the released membranes. In devices integrated on silicon, the interlayer stress provides deterministic control of ordering temperature (from 75 to 425 °C) and releasing the substrate clamping is shown to dramatically impact ferroelectric switching and domain dynamics (including reducing coercive fields to <10 kV cm and improving switching times to <5 ns for a 20 µm diameter capacitor in a 100-nm-thick film). In devices integrated on flexible polymers, enhanced room-temperature dielectric permittivity with large mechanical tunability (a 90% change upon ±0.1% strain application) is demonstrated. This approach paves the way toward the fabrication of ultrafast CMOS-compatible ferroelectric memories and ultrasensitive flexible nanosensor devices, and it may also be leveraged for the stabilization of novel phases and functionalities not achievable via direct epitaxial growth