GR-288 Comparative performance analysis of hybrid quantum machine learning algorithm to assess Post stroke rehabilitation exercises

Due to the advancements in technology, data is growing exponentially. With this increased dataset size, the computation to process the generated information is rising sequentially. And the currently available classical computational tools and learning algorithms will not work due to the limitations of Moore\u27s law. To overcome the computational issues, we have to switch to Quantum Computing which works based on the laws of Quantum Mechanics. Quantum Machine Learning (QML), a subset of Quantum Computing, is faster and more capable of doing complex calculations that a classical computer can\u27t. Classical Computers work on bits - 0 or 1, whereas a Quantum Bit (also known as a qubit) works on the superposition principle and can be 0 and 1 at the same time before it is measured. Other properties known as Quantum Entanglement, Quantum Parallelism, etc., also will help in understanding the other qubit state and parallel processing the data. In this paper, we introduce hybrid quantum and convolutional models built using PennyLane on the UI-PRMD dataset for the Kinect sensor. By involving quantum layers in a traditional network, a better performance can be achieved compared with the traditional neural network performance

The effect of scaffold physical properties on endothelial cell function

Thesis: Ph. D. in Materials Science and Medical Engineering, Harvard-MIT Program in Health Sciences and Technology, February 2010.Cataloged from PDF version of thesis.Includes bibliographical references (pages 135-139).Endothelial cells (EC) are ubiquitous - as vascular epithelial cells they line the inner surface of all vessels and are the contact surface with flowing blood. Macrovascular EC are the first line barrier between flowing blood and mural structures. The microvasculature includes EC-lined vessels that contact virtually every cell in the body. These EC are potent bioregulatory cells, modulating thrombosis, inflammation and control over mural smooth muscle cells and vascular health. The biochemical roles of EC can be retained when cells are embedded within three-dimensional matrices without recapitulation of the full vessel architecture. Within these matrices, surface and structural properties impose a set of forces on the embedded EC. Indeed, substrata pore size and modulus have profound effects on phenotype and function of a range of cell types. In the first part of this work, we examined the effect of pore size, matrix relative density and modulus on matrix-embedded EC growth and secretion and found a greater biological dependence on modulus than pore size or density. In the second part of this work, we examined the effect of isolated changes in modulus on BC growth, secretion of growth regulators, and modulation of smooth muscle cell growth. EC growth is maximal at intermediate moduli over a range from 50 Pa- 1500 Pa. Secretion of heparan sulfate proteoglycans (HSPGs), which inhibit smooth muscle cell growth, is maximal at low moduli and flat at high moduli. Secretion of growth factors such as FGF2 and PDGF-BB were also modulus responsive. Inhibition of smooth muscle cell growth rose as modulus decreased from 510 Pa to 50 Pa and was the result of a balance between increased HSPG secretion and reduced secretion of vasoactive growth factors. Changes in endothelial function correlated with extracellular matrix gene and integrin aP 3 and c41 expression. Changes in the forces experienced by the cell - a change in substrate modulus - cause the cell to alter its ECM and integrin expression in an effort to return the force balance to normal, leading to downstream effects on cell function. While growth stimulatory function largely conserved, growth inhibitory function was altered to a much larger degree. In the final part of this work, we examined the effect of scaffold modulus on EC response to inflammatory stimuli, and attempted to correlate it to changes in smooth muscle cell regulation and integrin expression. While cytokine secretion was independent of modulus, surface expression of ICAM- 1 and VCAM-1, and induction of CD4' T cell proliferation followed a similar pattern to smooth muscle cell inhibition, suggesting that similar mechanisms may be involved in their regulation by substrate modulus. Alteration of scaffold modulus has a profound impact on EC function including growth regulation and inflammatory response. The model offered in this thesis - wherein EC attempt to neutralize changes in environmental force balance by altering ECM and integrin expression, leading to changes in downstream function - offers insight into how environmental changes effect functional changes in ECs.by Sylaja Murikipudi.Ph. D. in Materials Science and Medical Engineerin

Brain-to-text communication through a non-invasive BCI

The purpose of the current study is to investigate the functionality of BCI’s to decode the attempted handwriting thoughts from neural activity in the motor cortex and translates it to text in real time. While there have been past studies regarding the efficacy of BCI’s the practical realization has been proven difficult due to limitations in accuracy and speed. Previous studies have approached this problem by using neural signals to choose from a limited set of possible words, this study seeks to have a more general model that can type any word in the vast English vocabulary. In this study, we create an end-to-end BCI that translates neural signals associated with visualization of the handwriting motion into text output. To assess the neural representation of attempted handwriting, participants attempted to handwrite each character one at a time, following the instructions given on a computer screen. The collected data from the EEG signals is challenging to process due to the noise and the similarities between different trials. To target the strategy for visualizing similarity relationships in high-dimensional data is to start by using an autoencoder to compress the data into a low-dimensional space, then use t-SNE for mapping the compressed data to a 2D plane. The TSNE technique that is applied serves to visualize the low-dimensional data. With this BCI, our study attempts to assist any individual that suffered any brain or physical damage that impedes the function of writing or that it affects the parietal lobes impeding the person of communicating

Post-stroke patients’ rehabilitation exercise assessment from Vicon-based skeletal angle displacement using Convolutional Neural Network

Stroke is one of the leading causes of neurological disorders, and around 1 million people suffer from stroke in the United States. Two-thirds of these individuals survive and requires rehabilitation exercise in their daily life to improve their quality of life. Automatically assessing these performed rehabilitation movements is inherent to improving post-stroke patients\u27 overall physical condition. With the recent growth in computer vision research, people are using motion capture systems to perform physical exercises, workouts, and training at their preferred place, as these systems occupy less space but provide flexibility to the users. This work assesses post-stroke patient rehabilitation movement from full-body skeletal joint displacement data sensed through vision-based Vicon sensors for ten exercises. We take advantage of transfer learning to strike the right balance between computation and performance. We propose a convolutional neural network (CNN) and train it using 117-dimensional skeletal angle displacement data from Vicon. This pre-trained convolutional neural network is fine-tuned for each post-stroke exercise movement. We use the publicly available rehabilitation exercise dataset to showcase the effectiveness and efficacy of our proposed simple CNN model. Our pretrained CNN model outperforms existing state-of-the-art complex Spatio Temporal Convolutional NN and achieves an average of 0.005795 MAD and 0.00786944 RMS error

Motor Imagery Detection Toward Non-Invasive Brainwave Based Typing

Electroencephalography (EEG) signals can be captured non-invasively with the help of Brain-Computer Interfaces (BCI). These EEG signals contain many essential information that can serve a great purpose when used correctly. By the appropriate interpretation of this EEG signal, we can provide people with limited ability to perform certain actions which they are unable to do due to their current condition. Paralyzed and semi-paralyzed people who are often found struggling to express themselves due to their medical condition can greatly benefit from the application of EEG. Typing or writing a letter requires functional motor movement. If we can detect the motor imagery movement from the EEG signal and determine the intent of the subject who is unable to perform motor functions but is imagining them, we can apply it to determine what they are trying to express in typed textual format. However, extracting features from EEG signals is incredibly challenging as EEG is susceptible to noise. Due to the absence of any informative association between the signals and the activity of the brain detecting motor movements and classifying them is difficult. Deep neural networks are proficient in understanding complicated features and performing computation which is very demanding. In this paper, we utilize the potential of deep neural networks to develop a model which is able to identify the motor imagery movement from the EEG signal of a subject. We envision to use this motor imagery obtained from the non-invasive brainwave to move the cursor using user thought to write letters and form words

Polymorphism in sulfadimidine/4- aminosalicylic acid cocrystals: solid-state characterization and physicochemical properties

YesPolymorphism of crystalline drugs is a common phenomenon. However, the number of reported polymorphic cocrystals is very limited. In this work, the synthesis and solid state characterisation of a polymorphic cocrystal composed of sulfadimidine (SD) and 4- aminosalicylic acid (4-ASA) is reported for the first time. By liquid-assisted milling, the SD:4-ASA 1:1 form I cocrystal, the structure of which has been previously reported, was formed. By spray drying, a new polymorphic form (form II) of the SD:4-ASA 1:1 cocrystal was discovered which could also be obtained by solvent evaporation from ethanol and acetone. Structure determination of the form II cocrystal was calculated using high resolution X-ray powder diffraction. The solubility of the SD:4-ASA 1:1 cocrystal was dependent on the pH and predicted by a model established for a two amphoteric component cocrystal. The form I cocrystal was found to be thermodynamically more stable in aqueous solution than form II, which showed transformation to form I. Dissolution studies revealed that the dissolution rate of SD from both cocrystals was enhanced when compared to a physical equimolar mixture and pure SD.Science Foundation Ireland (SFI) under Grant Number 07/SRC/B1158 and SFI/12/RC/2275

Phase I interim results of a phase I/II study of the IgG-Fc fusion COVID-19 subunit vaccine, AKS-452

To address the coronavirus disease 2019 (COVID-19) pandemic caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a recombinant subunit vaccine, AKS-452, is being developed comprising an Fc fusion protein of the SARS-CoV-2 viral spike protein receptor binding domain (SP/RBD) antigen and human IgG1 Fc emulsified in the water-in-oil adjuvant, Montanide™ ISA 720. A single-center, open-label, phase I dose-finding and safety study was conducted with 60 healthy adults (18–65 years) receiving one or two doses 28 days apart of 22.5 µg, 45 µg, or 90 µg of AKS-452 (i.e., six cohorts, N = 10 subjects per cohort). Primary endpoints were safety and reactogenicity and secondary endpoints were immunogenicity assessments. No AEs ≥ 3, no SAEs attributable to AKS-452, and no SARS-CoV-2 viral infections occurred during the study. Seroconversion rates of anti-SARS-CoV-2 SP/RBD IgG titers in the 22.5, 45, and 90 µg cohorts at day 28 were 70%, 90%, and 100%, respectively, which all increased to 100% at day 56 (except 89% for the single-dose 22.5 µg cohort). All IgG titers were Th1-isotype skewed and efficiently bound mutant SP/RBD from several SARS-CoV-2 variants with strong neutralization potencies of live virus infection of cells (including alpha and delta variants). The favorable safety and immunogenicity profiles of this phase I study (ClinicalTrials.gov: NCT04681092) support phase II initiation of this room-temperature stable vaccine that can be rapidly and inexpensively manufactured to serve vaccination at a global scale without the need of a complex distribution or cold chain

The effect of substrate modulus on the growth and function of matrix-embedded endothelial cells

Endothelial cells (EC) are potent bioregulatory cells, modulating thrombosis, inflammation and control over mural smooth muscle cells and vascular health. The biochemical roles of EC are retained when cells are embedded within three-dimensional (3D) denatured collagen matrices. Though substrate mechanics have long been known to affect cellular morphology and function and 3D-EC systems are increasingly used as therapeutic modalities little is known about the effect of substrate mechanics on EC in these 3D systems. In this work, we examined the effect of isolated changes in modulus on EC growth and morphology, extracellular matrix gene expression, modulation of smooth muscle cell growth, and immunogenicity. EC growth, but not morphology was dependent on scaffold modulus. Increased scaffold modulus reduced secretion of smooth muscle cell growth inhibiting heparan sulfate proteoglycans (HSPGs), but had no effect on secreted growth factors, resulting in a loss of smooth muscle cell growth inhibition by EC on high modulus scaffolds. Expression of ICAM-1, VCAM-1 and induction of CD4[superscript +] T cell proliferation was reduced by increased scaffold modulus, and correlated with changes in integrin α5 expression. Expression of several common ECM proteins by EC on stiffer substrates dropped, including collagen IV(α1), collagen IV(α5), fibronectin, HSPGs (perlecan and biglycan). In contrast, expression of elastin and TIMPs were increased. This work shows even modest changes in substrate modulus can have a significant impact on EC function in three-dimensional systems. The mechanism of these changes is not clear, but the data presented here within suggests a model wherein EC attempt to neutralize changes in environmental force balance by altering ECM and integrin expression, leading to changes in effects on downstream signaling and function.National Institutes of Health (U.S.) (R01 GM49039)Else Kroner-Fresenius Stiftung (P36/07//A45/07

Using NLP-based RCNN to Detect Suicide Indicators in Social Media Posts: A Proactive Approach to Lowering Suicide Rates

Suicide has become a significant issue within our society over the past few decades, so much so that 13.42% of 100,000 people will commit suicide within the year. When looked at on a larger scale, this is a drastic amount of people dying, which could be helped or stopped. This is shown by the percentage of a 25% lower suicide rate after having voluntary talk therapy. With an issue that can be helped by talking to a trained professional, it is better to find the person before they have gone too far. This research that we are conducting will help create a proactive way to help determine people for depression. A proactive approach will help lower suicide rates and give medical professionals a better chance at helping people in need before there is a chance to commit suicide. There have been leaps and bounds in machine learning in recent years, and using different databases, we can train an ML model with Natural Language Processing (NLP) techniques to determine if someone is considering suicide. We can train it using a 500 anonymized Reddit post dataset that has all the posts labeled based on the post’s relation to suicide. In this work, we propose to develop an NLP-based Recurrent Convolutional Neural Network (RCNN) to detect suicidal events such as suicide attempts and ideation. Using available word embeddings, we will represent Reddit posts as a feature vector and feed these features to an RCNN network to detect suicidal events. We envision comparing our model performance with traditional machine learning algorithms, such as Logistic Regression, Feed Forward Neural Networks, etc., to showcase the effectiveness of our algorithm. Keywords: Mental Health, Suicide, NLP, Recurrent Convolutional Neural Network, Feed Forward Neural Networ