NNAlign: A Web-Based Prediction Method Allowing Non-Expert End-User Discovery of Sequence Motifs in Quantitative Peptide Data

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new “omics”-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points

Design and development of an implantable biohybrid device for muscle stimulation following lower motor neuron injury

In the absence of innervation caused by complete lower motor neuron injuries, skeletal muscle undergoes an inexorable course of degeneration and atrophy. The most apparent and debilitating clinical outcome of denervation is the immediate loss of voluntary use of muscle. However, these injuries are associated with secondary complications of bones, skin and cardiovascular system that, if untreated, may be fatal. Electrical stimulation has been implemented as a clinical rehabilitation technique in patients with denervated degenerated muscles offering remarkable improvements in muscle function. Nevertheless, this approach has limitations and side effects triggered by the delivery of high intensity electrical pulses. Combining innovative approaches in the fields of cell therapy and implanted electronics offers the opportunity to develop a biohybrid device to stimulate muscles in patients with lower motor neuron injuries. Incorporation of stem cell-derived motor neurons into implantable electrodes, could allow muscles to be stimulated in a physiological manner and circumvent problems associated with direct stimulation of muscle. The hypothesis underpinning this project is that artificially-grown motor neurons can serve as an intermediate between stimulator and muscle, converting the electrical stimulus into a biological action potential and re-innervating muscle via neuromuscular interaction. Here, a suitable stem cell candidate with therapeutic potential was identified and a differentiation protocol developed to generate motor neuron-like cells. Thick-film technology and laser micromachining were implemented to manufacture electrode arrays with features and dimensions suitable for implantation. Manufactured electrodes were electrochemically characterised, and motor neuron-like cells incorporated to create biohybrid devices. In vitro results indicate manufactured electrodes support motor neuron-like cell growth and neurite extension. Moreover, electrochemical characterisation suggests electrodes are suitable for stimulation. Preliminary in vivo testing explored implantation in a rat muscle denervation model. Overall, this thesis demonstrates initial development of a novel approach for fabricating biohybrid devices that may improve stimulation of denervated muscles

Study on conductive hydrogels in flexible and wearable triboelectric devices towards energy-harvesting and sensing applications (エネルギーハーベスティングおよびセンシングに向けたフレキシブルでウェアラブルな摩擦発電デバイスにおける導電性ハイドロゲルに関する研究)

信州大学(Shinshu university)博士（工学）この博士論文は、次の学術雑誌論文を一部に使用しています。 / ACS Applied Materials Interfaces 14(7) :9126-9137(2022); doi:10.1021/acsami.1c23176 / Advanced Fiber Materials 4(6) :1486-1499(2022); doi:10.1007/s42765-022-00181-4 / Chemical Engineering Journal 457 :141276(2023); doi:10.1016/j.cej.2023.141276ThesisDONG, LI. Study on conductive hydrogels in flexible and wearable triboelectric devices towards energy-harvesting and sensing applications (エネルギーハーベスティングおよびセンシングに向けたフレキシブルでウェアラブルな摩擦発電デバイスにおける導電性ハイドロゲルに関する研究). 信州大学, 2023, 博士論文. 博士（工学）, 甲第802号, 令和05年03月20日授与.doctoral thesi

Microscale Measurements of Cell and Tissue Mechanics in Three Dimensions

Two-dimensional (2D) studies have revealed that mechanical forces drive cell migration and can feedback to regulate proliferation, differentiation and the synthesis/remodeling of extracellular matrix (ECM) proteins. Whether these observations can be translated to clinical settings or be utilized for tissue engineering will depend critically on our ability to translate these findings into physiologically relevant three-dimensional (3D) environments. The general goal of this dissertation has been to develop and apply new technologies capable of extending studies of cell and tissue mechanics into 3D environments. In the first project, we measured both shear and normal traction forces exerted by cells cultured on planar substrates. We observed that focal adhesions serve as pivots about which cells generate rotational moments. In the second project, we combined enzymatically degradable synthetic hydrogels with finite element models to measure the mechanical tractions exerted by cells fully encapsulated within 3D matrices. We found that cells reach out thin protrusions and pull back inward towards the cell body with the highest forces at the tip. Cellular extensions that were invading into the surrounding matrix displayed a strong inward force 10-15 microns behind the leading tip, suggesting that growing extensions may establish a contractile waypoint, before invading further. To study the forces cells exert during tissue remodeling, we utilized photolithograpy to generate arrays of microtissues consisting of cells encapsulated in 3D collagen matrices. Microcantilevers were used to constrain the remodeling of the collagen gel and to report the forces generated during this process. We used this technique to explore the effects of boundary stiffness and matrix density within model tendon and cardiac tissues. Finally, we combined this system with a Foerster radius energy transfer (FRET) based biosensor of fibronectin conformation to reveal how tissue geometry and cell-genereated tractions cooperate to pattern matrix conformation during tissue remodeling. Together, these studies highlight novel approaches to understand the nature of cell-ECM interactions in 3D matrices. Such mechanical insights will help us to understand how physical forces drive cell migration and behavior within physiologically relevant environments

Colloidosomes as a Protocell Model: Engineering Life-Like Behaviour through Organic Chemistry

The bottom-up synthesis of self-assembled micro-compartmentalised systems that mimic basic characteristics of living cells is rapidly evolving. These types of systems are termed “protocells” and can be chemically programmed to grow and divide, to send and receive chemical signals, to transcript and translate chemical information, to adhere to surfaces or to other protocells, and to perform rudimental enzyme-mediated metabolic processes. An emerging protocell model that is attracting great attention is the colloidosome. Colloidosomes are microcapsules with a chemically crosslinked, semipermeable membrane composed of amphiphilic nanoparticles. Colloidosomes display important advantages over other protocell models (e. g., vesicles and coacervate micro-droplets) due to their physical-chemical properties that can be easily tuned through the careful engineering of their synthetic building blocks. In this review, we deliver an overview of the different types of colloidosomes that have been developed thus far and discuss how organic chemistry contributes to the design and bottom-up synthesis of novel types of colloidosomes endowed with advanced chemically programmed bio-inspired functions

Reliable and Efficient Parallel Processing Algorithms and Architectures for Modern Signal Processing

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

Beyond Tissue replacement: The Emerging role of smart implants in healthcare

Smart implants are increasingly used to treat various diseases, track patient status, and restore tissue and organ function. These devices support internal organs, actively stimulate nerves, and monitor essential functions. With continuous monitoring or stimulation, patient observation quality and subsequent treatment can be improved. Additionally, using biodegradable and entirely excreted implant materials eliminates the need for surgical removal, providing a patient-friendly solution. In this review, we classify smart implants and discuss the latest prototypes, materials, and technologies employed in their creation. Our focus lies in exploring medical devices beyond replacing an organ or tissue and incorporating new functionality through sensors and electronic circuits. We also examine the advantages, opportunities, and challenges of creating implantable devices that preserve all critical functions. By presenting an in-depth overview of the current state-of-the-art smart implants, we shed light on persistent issues and limitations while discussing potential avenues for future advancements in materials used for these devices