Biomedical Engineering

Speaker: Jérôme Bernard Noailly

We are happy to invite you to the GCEC seminar with an external speaker, Jérôme Bernard Noailly from Pompeu Fabra University (Barcelona, Spain), who will give a talk on "Patient-specific and multi-scale organ modelling for stratification and prognosis purposes."

Abstract: While the burden of infectious diseases has decreased over the last decades globally, the one of Non-Communicable Disease and Disorders (NCD) continues increasing, in terms of premature loss of life quality and years of life. As such, NCD represent 70% of the top-10 disabling and life threatening diseases and disorders worldwide. Remarkably, more than half of these NCD affects mechanical load-bearing organs and tissues, and the identification of modifiable risk factors and of therapeutic targets is a major challenge. On the one hand, NCD can evolve subclinically over decades. On the other hand, the mechano-regulation of the cells that populate and regulate load-bearing tissues and organs makes pathophysiological processes be hugely multifactorial, driven by intricate physical, biophysical and biological phenomena. Computer models and simulations able to mechanistically represent key regulation phenomena over the scales are becoming increasingly valuable, to identify different pathophysiological mechanisms and stratify risk factors, for example in musculoskeletal joint degeneration. Yet, a large spectrum of the computer models and simulations must be employed, combining physics-based, biology-based, systems-based, and knowledge- and data-driven modelling. An overview will be given of the development, analysis and achievements of such heterogeneous modelling approach, in rheumatology.

Speaker: Dr Daniel Maitethia Memeu

Malaria remains a significant global health challenge, particularly in sub-Saharan Africa. Conventional diagnostic methods used for routine diagnosis suffer from several shortcomings, including being time-consuming and labor-intensive, having low detection sensitivity and specificity, and not being tailored for remote environments where malaria is endemic. In response to these challenges, our research focuses on developing a photonic-based, low-cost technology suitable for mass screening of malaria in sub-Saharan Africa.

This presentation will cover three key components:

1. Background Information about Malaria Disease: An overview of the malaria disease, its impact, and the limitations of current diagnostic methods.

2. Label-Free Sensing and Imaging: We have explored label-free techniques for the sensing and imaging of Plasmodium parasites in blood samples. By leveraging advanced photonic technologies, we aim to provide a rapid and cost-effective alternative to traditional staining methods.

3. Development of an RDT Reader Kit: Our team is working on a reader kit for rapid diagnostic tests (RDTs) that not only confirms the presence of malaria but also provides a quantitative diagnosis. This tool is designed to improve the accuracy of RDTs and facilitate better disease management and treatment outcomes.

Through this talk, I will share the promising results of our research and discuss the potential impact of these technologies on malaria diagnostics. I will also discuss some of the challenges we have encountered in the development of our research.

Biography

Dr. Daniel Maitethia Memeu is a physicist and lecturer at Meru University of Science and Technology, a public university in Kenya. He holds a B.Sc. and M.Sc. in Physics from the University of Nairobi and a Ph.D. in Physics from Kenyatta University. Dr. Memeu’s research interests lie at the intersection of photonics, biomedical imaging, and machine learning, with a specific focus on developing innovative, low-cost diagnostic technologies for resource-limited settings.

Currently, Dr. Memeu is spearheading a research initiative aimed at developing photonic-based, low-cost disease diagnostic technologies. He hopes to leverage collaborations to provide accurate and easily accessible technology for mass screening of malaria in Africa.

Speaker: Stéphane Holé

Sensor characteristics most often only give the conversion coefficient. This information is indeed important, but it only indicates who much physical information are converted into an electrical signal at the position of the sensor. In fact, any sensors are sensitive to their environment, the complexity being to calculate how specific physical information are transmitted to the sensor location: This is the sensor sensitivity map which not only gives the conversion coefficient at the position of the sensor, but also the conversion coefficient for a physical information at any position relative to the sensor.

In this presentation the sensor sensitivity map of capacitive sensors will be analytically calculated for all configurations (exact solution). The sensitivity field will be defined which, when numerically calculated only once for complexe configurations, will be able to give any signal variation for any modification in the environment of the sensor. This is a kind of Green's fonction for measurement. Exemples of application will be shown and a simple approximation will be described to estimate the 
second order which extends the range of use. The concept can be extended to almost any kind of sensors.

Speaker: Professor Nicole Pam

Nicole Pamme is a Professor in Analytical Chemistry at Stockholm University. She obtained her PhD at Imperial College London (UK) ,where she joined the group of Prof. Andreas Manz, a pioneer in microfluidics. She is a Fellow of the Royal Society of Chemistry (FRSC) and the Higher Education Academy (FHEA). Nicole is an Associate Editor for Analyst (RSC) and on the Editorial Advisory Boards of Analytica Chimica Acta (Elsevier), Lab on a Chip (RSC) and Analytical and Bioanalytical Chemistry (Springer). 

Her research revolves around the study of microfluidic lab-on-a-chip devices, which allow precise handling of liquids at microscopic scales. Her group applies lab-on-a-chip technology for environmental analysis on-site, for clinical diagnostics at the point-of-care and the synthesis of smart materials (see https://pammegroup.org for more details). This work was co-awarded the Newton Country Prize for Kenya (2020) for her work on point-of-care diagnostics for maternal health.

She also has a strong interest in outreach and engagement and developed a range of innovative hands-on activities to engage school children and the general public in our research in lab-on-a-chip based chemistry and microfluidic chemical measurement. 

Speaker: Professor Nicole Pam

Nicole Pamme is a Professor in Analytical Chemistry at Stockholm University. She obtained her PhD at Imperial College London (UK) ,where she joined the group of Prof. Andreas Manz, a pioneer in microfluidics. She is a Fellow of the Royal Society of Chemistry (FRSC) and the Higher Education Academy (FHEA). Nicole is an Associate Editor for Analyst (RSC) and on the Editorial Advisory Boards of Analytica Chimica Acta (Elsevier), Lab on a Chip (RSC) and Analytical and Bioanalytical Chemistry (Springer). 

Her research revolves around the study of microfluidic lab-on-a-chip devices, which allow precise handling of liquids at microscopic scales. Her group applies lab-on-a-chip technology for environmental analysis on-site, for clinical diagnostics at the point-of-care and the synthesis of smart materials (see https://pammegroup.org for more details). This work was co-awarded the Newton Country Prize for Kenya (2020) for her work on point-of-care diagnostics for maternal health.

She also has a strong interest in outreach and engagement and developed a range of innovative hands-on activities to engage school children and the general public in our research in lab-on-a-chip based chemistry and microfluidic chemical measurement. 

Speaker: Prof Huabing Yin, and Dr Jiabao Xu

Dear all,

The Division of Biomedical Engineering is organising a series of seminars called “BME PI Seminars”. The idea is to introduce the research undertaken by the principal investigators of the division to the whole James Watt School of Engineering and provide an opportunity for colleagues to build collaborations within the school.

Below is the information of our coming seminar:

Speakers: Prof Huabing Yin, and Dr Jiabao Xu.

Date: 15 March 2024

Time: 12:30 to 13:30

Location: Advanced Research Centre (ARC): 237C

or Zoom: https://uofglasgow.zoom.us/j/89920618919

Biography:

Professor Huabing Yin is Head of Biomedical Engineering Division at the University of Glasgow. Yin’s research focuses on developing advanced tools to study biological phenomena at the microscale level, which build upon the convergence of microfluidics, biointerface and advanced microscopic and spectroscopy technologies. She is an expert in single-cell analysis and has pioneered Raman-activated cell sorting technologies. These technologies have enabled a wide range of discoveries in cancer research, antibiotic resistance and environmental microbiology as well as novel devices.

Dr Jiabao Xu is a lecturer from Biomedical Engineering division. Before joining the University of Glasgow, she obtained her PhD and worked as a postdoctoral research scientist at the University of Oxford. Her research interest centres on the application of single-cell Raman spectroscopy, imaging techniques, cell sorting, and machine learning for biomedical and microbiology research. Currently, the main applications of the technology include identification of clinical pathogens, disease diagnosis, and stem cell characterisation. Her next proposal is focused on investigating post-infection chronic fatigue syndromes, including conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), long COVID, and chronic Lyme disease (CLD).

The seminar will be held monthly, and the information of next seminar will be announced nearer the time.

Best regards,

Chunxiao

Speaker: Norbert Radacsi

Associate Prof. Norbert Radacsi, The University of Edinburgh

Norbert Radacsi is a Senior Lecturer (Associate Professor) in Chemical Engineering and director of the NanoMaterials Laboratory at The University of Edinburgh. He obtained his Ph.D. at the Delft University of Technology. Prior to his current position, he was a postdoctoral researcher at Purdue University and California Institute of Technology (Caltech). He has pioneered a new type of 3D electrospinning technology. His interdisciplinary research includes areas of advancing electrospinning technology and bioprinting, developing wearable sensors and energy harvesters, and producing novel materials.

Speaker: Dr Aleksandra Vuckovic and Dr Haotian Chen

Dear all,

The Division of Biomedical Engineering is organising a series of seminars called “BME PI Seminars”. The idea is to introduce the research undertaken by the principal investigators of the division to the whole James Watt School of Engineering and provide an opportunity for colleagues to build collaborations within the school.

Below is the information of our coming seminar:

Speakers: Dr Aleksandra Vuckovic, and Dr Haotian Chen.

Date: 23 February 2024

Time: 12:30 to 13:30

Location: James Watt Building:427b Studio

or Zoom: https://uofglasgow.zoom.us/j/86849655288

Biography:

, PhD Biomed Eng, FIMechE is Reader in Rehabilitation Engineering. She holds MEng in Electrical Engineering and MSc in Control Systems from the University of Belgrade, Serbia and PhD in Healthcare technologies from the University of Aalborg, Denmark. She also has 8 years of experience as a designer of industrial process control systems in Belgrade, Serbia. Her research interest are in developing and testing neurotechnology for neurorehabilitation of patients with sensory-motor deficits. She is a founding member of the International Brain Computer Society, and the UK Knowledge Transfer Neurotechnology Interest group shaping the filed on the international and national level. She will present her research in the areas of diagnosis and treatment of chronic pain, analysis and rehabilitation of movements and gaming/improving peak performance in able bodied people.

  is a lecturer from Biomedical engineering division. Before joining University of Glasgow, he worked as a research scientist at EPFL in Switzerland, researching the application of soft electronics in neuroprosthetics. His research interests include wearable electronics, nanocomposites, exoskeletons & prosthetics, and teleoperation, with the specific focus on sense of touch. The goal is to explore advanced materials and stretchable electronic devices, and implement them to understand the mechanisms underlying sensorimotor impairment and develop assistive haptic technologies that mimic and restore the human sensorimotor system.

The seminar will be held monthly, and the information of next seminar will be announced nearer the time.

Speaker: Prof Paul Steinmann

Title: Modelling and Simulation of Bacterial Colony Formation: Challenges in Health and Computations

Speaker: Luigi G. Occhipinti

Abstract:  Printable sensors and electronic technologies are unique enablers of flexible, conformable, lightweight, skin-compliant, and bio-compatible integrated smart systems, for multi-sectorial applications, including personalised diagnostics and therapeutics.

Solely powering autonomous IoT devices with batteries may not sustain the growing complexity and size of the IoT ecosystem as it proceeds to one trillion nodes. For that leveraging energy storage with ambient energy harvesting technologies might help mitigate the sustainability challenge. Also, when it comes to sensors and electronics for data acquisition and processing, the design and development of ultra-low power printed electronic circuits and sensors, made of eco-friendly materials and manufacturing processes, plays a critical role in managing the power budget at the system level, and reducing the burden of batteries and conventional electronics to the environment.

Our recent works focus on thin-film, organic and graphene-based sensors. ambient energy harvesters and storage, ultra-low power printed electronics, and integration in flexible, stretchable and textile substrates, as suitable technologies for next generation flexible and wearable smart systems, alongside AI-based human data models and data processing.

In this talk, I will discuss design, advanced materials and heterogenous integration technology challenges and approaches for next-generation printed and flexible electronics, having sustainability as their key driving development factor, alongside functionality, bio/eco-compatibility, and end-user convenience.

Bio: 

Luigi Occhipinti has been managing research and innovation for over 25 years, encompassing multiple fields of engineering including sensors, biomedical devices, electronic design and manufacturing, advanced signal processing, AI and robotics, renewable energies, and nanomaterials.

Luigi is active both in academia and industry. After completing his PhD studies in 1995, he worked for over 18 years in the global semiconductor player SGS-Thomson Microelectronics (now STMicroelectronics), covering multiple roles in the organisation from team leader to group technical director, strategic alliances and R&D programs manager, with responsibility of teams located in Italy, France and Singapore.

Since 2014 he works at the University of Cambridge, Department of Engineering, where Luigi is currently the Director of Research in Graphene and Related Technologies and covering the role of Deputy Director and Chief Operating Officer of the Cambridge Graphene Centre.

Luigi is Senior member of IEEE, the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity, as well as member of the IEEE Engineering in Medicine and Biology Society (EMBS), the IEEE Electron Device Society (EDS), the American Chemical Society (ACS), the Materials Research Society (MRS), and sits in multiple advisory boards of multinational collaborative research programs, experts technology groups, and editorial boards of scientific journals and book series.

He also served in different standardisation committees, such as the IEEE P1620 on Organic Transistors and Materials, the IEEE P1620.1 on Organic Transistor-Based Ring Oscillators, the IEC/CEI CT105 on Fuel Cells technology, the IEC/CEI CT111 (Environmental Standardization for Electrical and Electronic Products and Systems), and the IEC/CEI CT113 (Nanotechnologies).

Luigi is the General Co-Chair of the IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) 2022 and former Technical Programme Chair since the first edition in 2019. He is also Programme Committee Member and former Chair of the Innovations in Large-Area Electronics Conference and Exhibition (innoLAE) since 2015.

The outcome of his research and innovation are captured in over 130 scientific publications in journals and conference proceedings, 3 book chapters, and over 65 patents and patent applications in 45 different patent families having Luigi as inventor or co-inventor (9 as sole inventor).

Speaker: Workshop

Over the past decade, paradigm shifts have been made towards the way healthcare is delivered and managed. Emerging pervasive sensing technologies, coupled with advanced data analytics, have enabled real-time personalised and environmental monitoring. These technologies have the potential to transform clinical practice in terms of diagnostics, prognosis, preventive healthcare, and rehabilitative/assistive technologies. To enable these systems, we need to understand the challenges in developing AI algorithms to handle heterogeneous, varying quality and imbalanced data. Academics, industry and NHS should work together to develop reliable and trustworthy technologies as well as Deep Learning brain-based algorithms that are able to handle and interpret continuous streams of heterogeneous, multi-modal data.

This workshop aims to bring together academics, industry and NHS related organisations with the goal to highlight opportunities for collaboration. We plan to:

Speaker: Ian Walker

Few global corporates have made such a strong commitment to supporting the communities in which they operate as the healthcare multi-national Johnson & Johnson - a company with a deep commitment to ethical principles. J&J work across four sectors of health and care, namely consumer health, medtech, pharmaceuticals and vision.

Ian, Corporate Citizenship Director at Johnson & Johnson, is responsible for the company’s citizenship programmes in Africa within the Johnson & Johnson Worldwide Corporate Global Community Impact team. He helps direct the company’s strategic philanthropy in a region of the world that has significant healthcare and humanitarian needs.

With a mission of making life-changing, long-term differences in human health, Johnson & Johnson’s World Wide Corporate Contributions partnership focuses on: saving and improving the lives of women and children; preventing disease in vulnerable populations; and strengthening the healthcare workforce. 

Examples of their work in Sub-Saharan Africa include strengthening maternal and child healthcare services in Uganda, support for disabled children in Kenya, SAFE Operating Rooms in Uganda, the integration of palliative care into community healthcare provision for South Sudanese refugees in Adjumani district, Uganda and more broadly collaboration with the Fistula Foundation, providing restorative care for young women.

Speaker: Raquel Mesquita

Speaker: Gael Varoquaux

Abstract:

Machine-learning, which can learn to predict given labeled data, bares many promises for medical applications. And yet, experience shows that predictors that looked promising most often fail to bring the expected medical benefits. One reason is that they are evaluated detached from actual usage and medical outcomes.
And yet, test runing predictive models on actual medical decisions can be costly and dangerous. How do we bridge the gap? By improving machine-learning model evaluation. First, the metrics used to measure prediction error must capture as well as possible the cost-benefit tradeoffs of the final usage. Second, the evaluation procedure must really put models to the test: on a representative data sample, and accounting for uncertainty in model evaluation. I will discuss advanced topic on these questions.
For medical applications, predictions should come with associated confidence. It is important to evaluate these confidence with adequate metrics. Here, the difficulty is to control individual probabilities, as each individual is observed only once. I will explain a procedure to measure how far a predictor is from outputing the ideal individual probabilities, due to intrinsic uncertainty [1].
Predictors can be used to reason about possible interventions: for a given individual, what is the potential outcome of an intervention versus no intervention? However, the corresponding inferences require a particular type of control on the error of the predictors [2].
Last but not least, a numerical experiment to benchmark predictors comes with arbritrary sources of variation. Understanding and accounting for this uncontroled variance is important to make well-grounded decisions on which predictive model to use. This is possible with simple procedures [3].


[1] Beyond calibration: estimating the grouping loss of modern neural networks
    Alexandre Perez-Lebel, Marine Le Morvan, Gaël Varoquaux
    ICLR 2023 – The Eleventh International Conference on Learning Representations, May 2023, Kigali, Rwanda
    https://proceedings.mlsys.org/paper/2021/hash/cfecdb276f634854f3ef915e2e980c31-Abstract.html

Biography:

Gael is a research director at the National Institute for Research in Digital Science and Technology (INRIA) at France. He is also the team leader of Soda - Computational and Mathematical Methods to understand health and society with data (https://team.inria.fr/soda/). His research interests encompasses three areas: 

• Machine learning and public health, which involves analytics on health databases for personalized medicine and treatment development, biomedical natural language processing and information extration and causal inference.
• Democratizing machine learning which encompasses machine learning on dirty data Missing data in machine learning, machine-learning model evaluation and learning on relational databases.
• Machine learning for mental health, cognition, and brain activity, which encompasses learning models of brain function and its pathologies from brain imaging, biomarkers of mental traits and disorders which encompasses resting-state and functional connectivity Encoding and decoding models of cognition.

 He is also the director of scikit-learn operations at Inria foundation and core contributor of several open source projects in scientific computing with python.  

Registration is required: https://uofglasgow.zoom.us/meeting/register/tZwrf-ygqjguE9NlUx9-uLqR0IEutLzPDe0K

Speaker: Prof. Crispin Miller

Prof. Crispin Miller is the head of bioinformatics at Cancer Research UK Beatson Insitute. He leads the Computational Biology group, which is focused on using data-driven approaches from machine learning to develop a better understanding of the processes that underpin tumour growth and development. 

A major aspect of his work is the use of cancer ‘omics data generated by large-scale tumour sequencing projects. These datasets are large enough to use machine learning algorithms that seek to correlate patterns with phenotype. This is allowing the team to explore aspects of tumour evolution, and to ask how the regulatory systems that control gene expression are perturbed in tumour cells.

His group is particularly interested in the regulatory pathways that act downstream of transcription, including the processes that govern how alternative splicing is coordinated across different pathways. Other projects in the group focus on uncovering novel regulatory sequences within the genome, and in making use of comparative genomics to help interpret the genome rearrangements that occur in tumour cells.

Registration is required: https://uofglasgow.zoom.us/meeting/register/tZUtf-msqjIoEtQhY63wGchI-Vaj42GueFsS

Speaker: Prof. Nasir M. Rajpoot

Abstract:

• Large collections of pathology image data offer a potential goldmine of invaluable information, ripe for deep learning of known and deep mining of novel digital histological biomarkers of cancer diagnosis, prognosis, clinical outcome and response to therapy.
• This talk will cover some of the major challenges faced and opportunities offered by the nascent discipline of computational pathology.

Biography:

Nasir Rajpoot is Professor of Computational Pathology at the University of Warwick and Honorary Scientist at the Department of Pathology, University Hospitals Coventry & Warwickshire (UHCW) NHS Trust. Prior to completing his PhD in Computer Science from Warwick in 2001, he was a Postgraduate Research Fellow in the Applied Math program (partially based at the School of Medicine) at Yale University (USA) during 1998-2000 and a Systems Engineering Fellow at PIEAS (Pakistan) during 1994-1996.

Prof Rajpoot is the founding Director of Tissue Image Analytics (TIA) Centre (previously the TIA lab) at Warwick since 2012 and also co-Director of the recently funded £15m PathLAKE centre of excellence on AI in pathology since Jan 2019. The focus of current research in TIA Centre led by Prof Rajpoot is on AI and machine learning algorithms for the study of histological and multi-omic markers of cancer biology, with applications to early detection of cancer and stratification of cancer patients in terms of recurrence, progression and response to therapy. He has been active in the digital pathology community for almost two decades now and has delivered over 80 invited and keynote talks since 2015 at various national and international events and institutions.

Prof Rajpoot recently served as President of the European Congress on Digital Pathology (ECDP), which took place at Warwick in April 2019. Previously, he served as the General Chair of the UK Medical Image Understanding and Analysis (MIUA) conference in 2010 and as the Technical Chair of the British Machine Vision Conference (BMVC) in 2007. He co-chaired several meetings in the histology image analysis (HIMA) series since 2008 and served as a founding PC member of the SPIE Digital Pathology meeting since 2012. He is a Senior Member of IEEE and member of the Association of the Computing Machinery (ACM), the British Association of Cancer Research (BACR), the European Association of Cancer Research (EACR) and the American Society of Clinical Oncology (ASCO).

Prof Rajpoot was recently awarded the Turing Fellowship by the Alan Turing Institute, the UK's national data science institute.

Speaker: Dr Simon Rogers

NHS NSS is a specialist health board, providing services to the other health boards and across the public sector. Within NSS, the Business Intelligence (and more specifically, the Artificial Intelligence Centre of Excellence) provide assistance, expertise, and a technology platform to support projects with an AI component. In this talk, I will introduce NSS BI (including our Seer platform), and discuss some of the work that we are undertaking, including work on the new version of the SPARRA model (predicting admission / re-admission for individuals in Scotland), work on how to safely export trained models from safe-haven environments, and some ongoing work on fairness / inequalities.

Biography:

Dr Simon Rogers is an AI data scientist at NHS National Services Scotland and Honorary Lecturer in Computer Science at University of Glasgow. His research involves the development of Machine Learning and Statistical techniques to help with the analysis of complex datasets. Among his long publication list he has also published a textbook on 'A First Course in Machine Learning', which is an introduction to modern (fairly probabilistic) Machine Learning.

This is a hybrid seminar: Join us at 423 Sir Alwyn Williams Building, School of Computing Science.

Registration is required:  https://uofglasgow.zoom.us/meeting/register/tJUkcOqsqz8vHdboNnP6P-_L3F5na54lHlsx

Speaker: Alexandra Dobrea

Speaker: Dr Silvia Caponi

Speaker: Dr. Simone Stumpf

Abstract:

AI is touted as the panacea that will cure all the ills of the current healthcare system. In this talk I will provide an overview of my work in technologies for healthcare, well-being and accessibility that will draw out lessons learned and challenges for designing and developing effective healthcare technologies that use AI.

Biography:

Dr. Simone Stumpf recently joined University of Glasgow, UK, as a Reader in Responsible and Interactive AI. She has a long-standing research focus on user interactions with machine learning systems. Her research includes self-management systems for people living with long-term conditions, developing teachable object recognisers for people who are blind or low vision, and investigating AI fairness. Her work has contributed to shaping the field of Explainable AI (XAI) through the Explanatory Debugging approach for interactive machine learning, providing design principles for enabling better human-computer interaction and investigating the effects of greater transparency. The prime aim of her work is to empower everyone to use AI systems effectively.

Speaker: Dr Xianghua Ding & Dr Marwa Mahmoud

This is a special hybrid event that hosts Dr Marwa Mahmoud that they are going to talk about their research on 'Advance Technology, Empower People: Exploring Social-Technical Approaches for Everyday Health' and 'Vision-focused multimodal behaviour modelling for mental health applications'

In person location: 422-423 Sir Alwyn Williams Building, School of Computing Science, UoG

Online location: https://uofglasgow.zoom.us/meeting/register/tJUucOCrpjovHdLqvVrzDE4XWTHqH9BtT5uP 

Title: Vision-focused multimodal behaviour modelling for mental health applications

Abstract:

There is a growing interest from healthcare organisations, academia and industry on automatic prediction, prevention and intervention of mental health issues, but most of the current work depends  on non-visual input, such as wearables and mobile phone data or basic analysis of video focussing on facial expressions analysis only. These models do not capture the full picture and overlook complex non-verbal behaviour analysis, which is the basis of many mental disorder diagnoses. Computer vision techniques have not been fully explored in this domain because of the sensitive nature of the data and limited availability of public datasets for training. Moreover, the high dimensionality of the signals collected from video complicates processing.

In this talk, I will present my work on automatic analysis and detection of body gestures and self-adaptors that are related to psychological distress. I will also discuss multimodal behaviour modelling techniques that I adapted to overcome the challenges of data scarcity and noisy complex signals in mental health datasets. Finally, I will also present some of my recent work on modelling gestures and body expressions in infants shedding light on their relationship with their neurodevelopment assessment.

Biography:

Dr Marwa Mahmoud is a Lecturer in Socially Intelligent Technologies in the School of Computing Science at University of Glasgow, and a Visiting Fellow in the Department of Computer Science and Technology at University of Cambridge, UK. Before joining University of Glasgow, she spent 10 years at University of Cambridge, where she obtained her PhD in 2015, then worked as a postdoc for a year before managing to secure the prestigious King’s College Junior Research Fellowship in 2016, which allowed her to start independent research. Her research interests focus on computer vision for social signal processing and multimodal signal processing, especially within the context of affective computing, behaviour analytics, human behaviour understanding and animal behaviour understanding. She applied her research in the areas of automotive applications, mental healthcare, and animal welfare. She is interested in ‘AI for Social Good’, combining computer vision research with health for human well-being and animal welfare applications. She is an elected Executive Committee (EC) Member of the Association for the Advancement of Affective Computing (AAAC), Network Member of Cambridge Trust & Technology Initiative and a member of Cambridge Neuroscience.

Title: Advance Technology, Empower People: Exploring Social-Technical Approaches for Everyday Health

Abstract:

Biography:
Dr. Xianghua (Sharon) Ding is a Senior Lecturer in Healthcare Technologies in the School of Computer Science at the University of Glasgow. Before moving to Glasgow, she was an Associate Professor at Fudan University, Shanghai, China. She received her Ph.D. from University of California, Irvine, USA in 2010. Her research falls in the areas of Human Computer Interaction(HCI), Computer Supported Cooperative Work (CSCW), and Ubiquitous Computing (Ubicomp). She is interested in social-technical approaches to empower people to manage their health in daily lives, with particular interest in intelligent health sensing and collaborative technologies for preventive health, health literacy, and mental wellbeing. Her work has been published at flagship venues in HCI such as CHI, CSCW, and Ubicomp, and has received Best Paper (CSCW2015) and Honorable Mention (CHI2020) awards. She has also been serving leadership roles in these venues, including Editor for CSCW2021-2022, Subcommittee Chair for CHI2021’s Health and CHI2019’s Understanding People, and Associate Chair for CHI and CSCW. She also led the publication on a special issue on human-centered cooperative computing as a lead guest editor for CCF’s Transactions on Pervasive Computing and Interaction.

Speaker: Dr. Stamatia Giannarou

This is a special session organised on behalf of the Computing Technologies for Healthcare Theme and the Athena Swan. The session includes:

• Research talk and Q/A (60 minutes)
• Discussion on career progression and fellowships (45 minutes) 

Registration is required:  https://uofglasgow.zoom.us/meeting/register/tJwsduqhrDwiHtdMKCADLEHgdNagRGveehZr

Consider subscribing to our emailing list: https://samoa.dcs.gla.ac.uk/events/series.jsp?series=179 

Abstract: With recent advances in medical imaging and surgical robotics, surgical oncology is entering a new era that is set to bring major healthcare and socio-economic benefits. The main goal of surgical oncology is to achieve complete resection of cancerous tissue with minimal iatrogenic injury to surrounding tissue. In practice, this often presents a formidable challenge to surgeons. Surgery on tumours residing within the brain is particularly demanding, and the prognosis for patients afflicted with such tumours remains very poor. Intrinsic brain tumours are highly infiltrative making it difficult to distinguish tumour tissue from surrounding tissue. Moreover, it is imperative to preserve unaffected brain tissue, which is delicate, often eloquent, and has little capacity for regeneration.

The aim of my research is to integrate multimodal intraoperative imaging and navigation technologies into a cognitive robotic platform. In this talk, I will present an intraoperative vision system for surgical navigation and real-time tissue characterisation during robot-assisted neurosurgery to improve both the efficacy and safety of tumour resections. The focus will be on the recovery of 3D morphological structures in the presence of tissue deformation, the efficient robot-assisted tissue scanning with imaging probes and the tissue characterisation for on-line diagnosis support.

Biosketch: Stamatia (Matina) Giannarou received the MEng degree in Electrical and Computer Engineering from Democritus University of Thrace, Greece in 2003, the MSc degree in communications and signal processing and the Ph.D. degree in image processing from the department of Electrical and Electronic Engineering, Imperial College London, UK in 2004 and 2008, respectively. Currently she is a Royal Society University Research Fellow and a Lecturer in Surgical Cancer Technology and Imaging at the Hamlyn Centre for Robotic Surgery, Department of Surgery and Cancer, Imperial College London, UK. Her research focuses on enhanced surgical vision for intraoperative navigation in minimally invasive and robot-assisted operations. In 2017, she won “The President’s Award for Outstanding Early Career Researcher” at Imperial College London. She has been selected as a member of the IdeasLab of Imperial College London on the “Frontiers of Imaging” at the World Economic Forum Annual Meeting of the New Champions 2016 in Tianjin, China. She received best paper awards at international conferences and workshops including the IPCAI 2016, AE-CAI-MICCAI 2020, IPCAI 2020, AE-CAI-MICCAI 2021. She has also been invited to present her work at a number of international workshops and symposia. She is a regular reviewer for high impact journals and conferences in the fields of medical robotics, medical imaging and biomedical engineering and the chair of the annual Hamlyn Winter School on Surgical Imaging and Vision.

Speaker: Dr. Karim Lekadir

Abstract: Amid hope and hype, artificial intelligence (AI) is widely regarded as one of the most promising and disruptive technologies for future healthcare. The application of medical AI has the potential to increase the productivity and efficiency of clinicians, improve medical diagnosis and treatment, optimise the allocation of human and technical resources, and lead to better health outcomes for patients and citizens. However, there are currently several technical, clinical, ethical and legal risks associated with medical AI that have limited its deployment in the real world. This talk will discuss these risks, including the potential lack of clinical safety, the limited generalisability of the AI solutions across settings, and important ethical issues such as algorithmic bias against under-represented groups. I will also discuss the need for guidelines and best practices to support the design, development and deployment of future AI tools in healthcare that are accurate and robust, but also trustworthy and ethical, to maximise their acceptance and adoption by medical professionals and patients alike. 

Biography:  Dr. Karim Lekadir is a Ramon y Cajal Researcher and Director of the Artificial Intelligence in Medicine Lab at the Universitat de Barcelona (BCN-AIM). He holds a PhD from Imperial College London (UK) and was previously a visiting scholar at Stanford University (USA). His current research focuses on the development of data science and machine learning approaches for the analysis of large-scale biomedical data, including imaging, clinical, lifestyle, and mobile data. The software he developed during his PhD for cardiac functional quantification has been CE marked and commercialised by CMRtools, and is now used in more than 250 clinical centres worldwide. He is the Coordinator of the following Horizon 2020 projects: euCanSHare (2018-2022), developing a big data platform for cardiovascular research; EarlyCause (2019-2023), which investigates multi-morbidity using experimental and data science approaches; and EuCanImage (2020-2024), which is building a federated artificial intelligence environment for cancer imaging. He is also work package leader in the longITools H2020 project  (2019-2024), developing a mobile app for cardio-metabolic risk prediction based on exposome data. In addition, Karim is General Chair for the MICCAI 2024 Conference (Medical Image Computing and Computer-Assited Intervention) which for the first time will take place in Africa – in Marrakesh, Morocco. He is an Associate Editor of IEEE Transactions on Medical Imaging.

Speaker: Andrew Simpson

Computer Science career in the NHS: The Scientist Training Programme (STP)

Andrew Simpson will join us on Zoom on the 25th of January at 6:30pm to talk about the Scientist Training Programme in the NHS. The STP is a three year fully NHS funded training programme, leading to an Clinical Scientist. During training, the student is paid at NHS Band 6 (at a training Annex). The entry requirements are: An undergraduate degree in either Computer Science, Mathematics or Scientific Engineering.

Computer Scientists who are Clinical Scientists can and do: 

• Develop medical software and technology
• Use that software/technology clinically on patients (in theatres, on wards, etc.)  (Under Annex A of the Medical Device Regulations) 
• Understand and be involved in the research of the science behind the diagnostic test
• Advise doctors on the results and on the technology available for patients.

All as a state registered healthcare professional in a truly unique, in demand and rewarding role.

Higher Specialist Scientific Programme (HSST)

As your career progresses, Clinical Scientists are eligible to further train as Consultant Clinical Scientists in Clinical and Scientific Computing, to demonstrate significant expertise at the intersection of Computer Science and Healthcare.

STP Equivalence
This is portfolio route by the Academy of Healthcare Sciences, where the pre-registrant demonstrates equivalency to the Scientist Training Programme outcomes. A master's degree is not required although the pre-registrant must demonstrate working to master's level.

Route II
This route allows a work-based competency learning route for those who have a masters and relevant experience. The pre-registrant works towards and compiles a portfolio of evidence, demonstrating competency in Clinical Computing to the level of Clinical Scientist.

About Andrew:

He studied BSc & MSc Computer Science, worked in IT industry for a while and he is now training as a clinical scientist in physiological measurement & clinical computing, under Route II, in the James Cook University Hospital's Medical Physics Department.

(For more information email Dr. Tim Storer: timothy.storer@glasgow.ac.uk)

Speaker: Prof. Frederick Matsen

Phylogenetic (evolutionary tree) inference is a key tool for understanding evolutionary systems. This includes viral adaptation and genomic epidemiology, as well as the antibody response to infection and vaccination. Bayesian phylogenetic analysis allows us to assess and integrate out tree uncertainty to obtain more reliable estimates of other model variables of interest (e.g. transmission rates). However, Bayesian posterior distributions on phylogenetic trees remain difficult to sample despite decades of effort. The complex discrete and continuous model structure of trees means that recent inferential methods developed for Euclidean space are not easily applicable to the phylogenetic case. Thus, we are left with random-walk Markov Chain Monte Carlo (MCMC) with uninformed tree modification proposals; these traverse tree space slowly because phylogenetic posteriors are concentrated on a small fraction of the very many possible trees.

In this talk, I will give a relatively non-technical overview of the work we have done to enable Bayesian phylogenetic inference via optimization. This work has led to a new discrete inferential target, which we call the "subsplit directed acyclic graph," and a new algorithm that will allow us to infer this structure using methods analogous to much faster maximum-likelihood (point-estimate) methods for phylogenetics. I will also describe how, once this structure is in hand, we can perform variational inference for continuous parameters via stochastic gradient descent. 

Biography:

Dr. Frederick “Erick” Matsen is an expert in computational biology, which is the science of using biological data to develop computer algorithms, or programs, to understand biological systems and relationships. His research team has developed new methods to analyze data generated by sequencing the DNA of viruses, immune cells and complex environmental samples containing many microorganisms. The team also pursues more abstract questions about the methods used to construct evolutionary trees. Another focus of Dr. Matsen’s work is on improving software used in computational biology, both by developing open source tools and by contributing to work on larger, collaborative projects.

Registration: https://uofglasgow.zoom.us/meeting/register/tJYsf-2tpjkqHte7oAoHA8svaCuDku4j0eJ8  

Speaker: Dr. Alison ONeil

Radiologists are under pressure to handle an ever-increasing volume of medical imaging. In the meantime, deep learning solutions have demonstrated impressive performance for imaging tasks of classification, segmentation and translation, opening up opportunities for AI to assist and automate imaging workflows. Training deep learning solutions for real-world healthcare applications involves ethical, legal and practical performance considerations. As we look to scale deep learning to a wide range of clinical applications, we also need to find ways to reduce reliance on pixel-level expert annotations whilst retaining clinically acceptable accuracy. This talk will cover some of Canon’s research in this area.

Biography:

r Alison Q O’Neil is a Principal Scientist in the AI Research Team at Canon Medical Research Europe and Honorary Research Fellow at the University of Edinburgh. She leads an AI Research team at Canon who focus on imaging and natural language processing for healthcare problems. Her research interests span techniques for learning with less, multimodal learning, knowledge informed machine learning, and causality.

 Registration is required to attend the event: https://uofglasgow.zoom.us/meeting/register/tJUuf-2prD0sG91BDJPMlW9HKqBz6itv8CgP 

Speaker: series hosted by Dr. Aleksandra Vuckovic

Programme and Zoom details below: 

12:30-1 p.m. Dr Mariel Purcell MD, NHS, Director of the Queen Elizabeth National Spinal Injuries Unit, Glasgow : “Scottish Centre for Innovation in Spinal Cord Injury: research activities and a journey through patient recovery “ 

1:00-1:20 p.m. Ms Claire Lincoln, QENSIU & Glasgow Caledonian University, “'The role of physiotherapy in spinal cord injury'

1:20 p.m. -200 p.m. Dr Aleksandra Vuckovic , University of Glasgow “Sensory motor neurorehabilitation and neuroimaging diagnostic”

Break 2:00-2:10 p.m.

2:10-2:40 p.m. Professor John Riddell, University of Glasgow “Mechanisms of neuropathic pain after spinal cord injury and the role of the spinothalamic tract”

2:40-3:10 p.m. Dr Samira Saadoun, Dr Marios Papadopolous, St George’s University of London “Acute spinal cord injury: monitoring from the injury site and the DISCUS trial.

3:10-3:30 p.m. Dr Bethel Osuagwu, University of Glasgow: “Implementation of active engagement in motor rehabilitation”

Break: 3:30-3:40 p.m.

3:40-4:00 p.m. Dr Henrik Gollee, University of Glasgow: “Ultrasound Imaging of muscle and abdominal stimulation for breathing in SCI”

4:00-4:20 p.m. Dr Euan McCaughey, QENSIU & Neura, Australia “From Glasgow to the world: collaborative research at the QENSIU”

4:20-4:40 p.m. Dr Sylvie Coupaud, University of Strathclyde “Current understanding of disuse osteoporosis after spinal cord injury and rehabilitation options to counteract it”

4:40-5:00 p.m. Looking into the future

Zoom details: 

https://uofglasgow.zoom.us/j/96778345264?pwd=c25kTzlYWFRvVFRHeU5BWlQzdWcvdz09

Meeting ID: 967 7834 5264

Passcode: 925540

One tap mobile

+442034815237,,96778345264#,,,,*925540# United Kingdom

Speaker: Ben Seymour

Pain is an extremely useful sense, providing a sensory signal that warns us about impending tissue damage. What makes this an especially powerful sense is its ability to drive learning: allowing us to learn from painful mistakes to avoid harm in the future; and after an injury, shaping the way we change our behaviour to maximise protection and encourage recuperation. In the brain, the organisation of the pain systems seems to reflect its core role as a learning and control signal, and in my talk I will outline some of the computational and neurobiological findings that speak to a hierarchical organization of control systems. A central facet of this is the way in which the system tunes the perception of pain - termed endogenous modulation - to maximise the effectiveness of learning. However, it also seems likely that the learning processes that protect us after injury may make us susceptible to chronic pain, and I'll discuss evidence to support a role of brain learning mechanisms in clinical pain groups. As a corollary, however, this may provide insight into how learning-oriented technologies could help patients recover from chronic pain.

Meeting URL: https://uofglasgow.zoom.us/j/93802302838?pwd=czVYUkZyV0UzblhTMXBlRm9ZVTNxZz09

Zoom Meeting ID:938 0230 2838

Passcode:571802

Speaker: Prof Daniel Hurtado

Title: Multiscale modeling of lung biomechanics

Abstract: Covid-19 pneumonia has quickly become a leading cause of death worldwide, boosting the interest of the scientific computing community in creating accurate models of the respiratory system for in silico experimentation and medical discovery. In this talk, I will present our current efforts towards creating a multiscale framework to achieve whole-lung predictive simulations. Drawing concepts from finite-deformation homogenization theory, I will introduce a microstructural model for the poroelastic behavior of the lung tissue. I will further discuss our validation efforts and comment on the predictiveness of the tissue model. Finally, I will present how the proposed micromechanical model can be integrated into whole-lung simulations of healthy and diseased conditions and discuss future directions in lung modeling.

Short Bio: Daniel Hurtado is an associate professor with the School of Engineering and the Institute for Biological and Medical Engineering at Pontificia Universidad Catolica de Chile. He leads the Computational Medicine Group, an interdisciplinary team that focuses on creating physiology-based digital replicas of the human lungs, with applications in the study of mechanical ventilation therapies and early diagnosis of pulmonary diseases. Prof. Hurtado received his M.S. and Ph.D. degrees from the California Institute of Technology as a Fulbright fellow. He is an elected member of the World Council of Biomechanics since 2018.

Zoom link: https://uofglasgow.zoom.us/j/92841584390?pwd=aHZFYUtBS2kyU0duK3ZmanVEeXl4Zz09

Speaker: Dr Andrew Kerr

Speaker: Giuseppe Spoto

Standard clinical protocols for evaluating tumour profiling are usually based on tissue biopsy, which consists of sampling cells from the human body using special needles or surgery. Tissue biopsy constitutes a significant barrier for easy and frequent monitoring of cancer patients and is subject to limitations, including the difficulty in accounting for tumour cells heterogeneity. In a liquid biopsy, biological fluids are instead sampled to monitor the level of cancer biomarkers available in bodily fluids such as peripheral blood and blood-derived products such as plasma and serum. The detection of nucleic acid biomarkers for cancer diagnosis and patient follow-up based on liquid biopsy represents a challenging task for current biosensing platforms. Recently, several methods for detecting blood cancer mutations have been proposed, generally relying on multi-step and PCR-based, time-consuming and cost-ineffective procedures. PCR suffers from artefacts generated by sample contamination and recombination between homologous regions of DNA. Efforts have been made to identify innovative PCR-free protocols for DNA detection. Most of such protocols exploit strategies for signal amplification based on the use of enzymes or metallic nanostructures. In particular, gold nanoparticles have been used to achieve the ultrasensitive detection of DNA. Possibilities offered by nanoparticle-enhanced surface plasmon resonance imaging (SPRI) in the detection of non-amplified human genomic DNA and DNA freely circulating in human blood will be discussed in the context of applications to cancer diagnosis based on liquid biopsy. By exploiting a liquid biopsy approach, we developed an ultrasensitive nanoparticle-enhanced plasmonic method for detecting attomolar tumour DNA in the plasma of colorectal cancer patients. The assay does not require the extraction of tumour DNA from plasma and catches it in volumes as low as 40 mL of plasma, which is at least an order of magnitude smaller than that required by state of the art liquid biopsy technologies. The assay was proven in plasma from CRC patients and healthy donors, and full discrimination between mutated DNA from patients over wild-type DNA from healthy volunteers was obtained, thus demonstrating its promising avenue for cancer monitoring based on liquid biopsy.

The presented work is part of the ULTRAPLACAD Horizon 2020 project (project n. 633937) activities. 

------------------------------

Giuseppe Spoto is a Professor of Analytical Chemistry at the University of Catania. He is also an Executive Committee member of the Biostructures and Biosystems National Institute (INBB). He received his PhD in Chemical Sciences from the University of Catania. His primary research focuses on the development of innovative detection methods and assays. Plasmonic biosensing and microfluidics are today used in his lab to design new assays to detect biomarkers freely circulating in the blood of cancer patients. His research has been primarily supported by grants from the European Commission and the Italian Ministry of Education, University and Research (MIUR), and he is the author and co-author of over 100 journal articles and book chapters. Giuseppe acted as the scientific coordinator of the ULTRAPLACAD (Ultrasensitive plasmonic devices for early cancer diagnosis) Horizon 2020 project, cited as an example of what Europe does for cancer patients in the European Parliament portal “What Europe does for me”.

Speaker: Dr. Mario González Jiménez

Abstract

Anopheles gambiae and Anopheles arabiensis mosquitoes are the primary vectors of malaria in Africa. These mosquitoes, which often live sympathetically, are morphologically indistinguishable despite their large differences in behaviour and ecology (willingness to enter and rest in houses, biting behaviour, use of hosts, breeding conditions, resistance to insecticides, etc.) that mark vector control strategies. In addition, their vector competence depends on their age, for the simple reason that the period of development of the parasites in their body after biting an infected person is usually more than 10 days. [1]
However, despite the need in the field, an efficient method to characterise the age or species of large numbers of mosquitoes is still lacking. Currently, the species is determined by expensive PCR methods and the age by microscopic analysis of their ovaries using dissection.
We developed a cheap, fast method that does not require highly trained personnel to perform. [2] It consists in the employment of machine learning algorithms to model the changes that occur in the infrared spectrum of the cuticle with age and between species. After examining the IR spectra of more than 40,000 mosquitoes from Burkina Faso, Tanzania, and labs in Scotland, our method can currently predict the age and species of field mosquitoes with 89% and 95% accuracy, respectively.

Speaker: Dr. Kotaro Hiramatsu,

Abstract: 

Speaker: Prof Alexander Grigorenko

 Abstract:

“We will discuss plasmonic surface lattice resonances which appear when metal nanoparticles are arranged in ordered arrays. If one of the diffracted waves propagates in the plane of the array, it may couple the localized plasmon resonances, leading to an exciting phenomenon of the drastic narrowing of plasmon resonances down to 1−2 nm in spectral width. This presents a dramatic improvement compared to a typical single particle resonance line width of >80 nm. The very high quality factors of these diffractively coupled plasmon resonances and related effects have made this topic a very active and exciting field for fundamental research, and increasingly, these resonances have been investigated for their potential in the development of practical devices for communications, optoelectronics, photovoltaics, biosensing, and other applications. We describe the basic physical principles and properties of plasmonic surface lattice resonances and pay special attention to the conditions of their excitation in different experimental architectures by considering the following: in-plane and out-of-plane polarizations of the incident light, symmetric and asymmetric optical (refractive index) environments, the presence of substrate conductivity, etc. We will also review recent progress in applications of plasmonic surface lattice resonances in various fields.”

Biography:

Prof. Grigorenko graduated from Moscow Physical Technical University in 1986 and got his PhD in 1989. He worked in the General Physics Institute for 10 years under the guidance of A. M. Prokhorov – laser inventor. After a spell as a postdoc in Bath and Plymouth Universities, he became a Lecturer and then Professor at the University of Manchester (from 2002). Prof. Grigorenko enjoys science in general and optics in particular.

Meeting link:

https://uofglasgow.zoom.us/j/97931764773?pwd=ZS9oUFI5YUNqZXNZWktvK28xK29Vdz09

Speaker: Yazi Al'Joboori, Sean Doherty and Anne Vanhoestenberghe

An overview of work undertaken at UCL's Aspire Centre for Rehabilitation Engineering and Assitive Technologies (CREATe) exploring a range of applications of electrical stimulation and neuromodulation with a focus on people living with a spinal cord injury. We will present results from studies on the effects of spinal stimulation (SS) and Functional Electrical Stimulation (FES) on descending motor control in able-bodied subjects, and combining FES and SS with physical rehabilitation strategies such as sit-to-stand training (STIM2STAND) and cycling with virtual reality (iCycle) to enhance functional recovery in SCI populations. We will also present work on the effects of non-invasive electrical stimulation on bladder overactivity following spinal cord injury, looking to translate successful techniques into practicable, wearable devices for long-term use. Our non SCI work includes an EMG control algorithm for an artificial larynx replacement and studies, in healthy volunteers, on the metabolic impact of electrical stimulation. Finally, we will briefly present a few technical elements in earlier stages of development.

Speaker: Dr Radu Boitor

Clinical implementation of a Raman spectroscopy device for detection of residual basal cell carcinoma during skin surgery 

Surgical removal is the main treatment method for basal cell carcinoma (BCC). The completeness of tumour removal is routinely assessed via histopathology, which can be used to identify tumours with high sensitivities and specificities. However, its intra-operative use during tissue-conserving surgery (such as in Mohs surgery) is limited by time-consuming tissue preparation steps and the diagnostic variability inherent in subjective image interpretation. 

We have developed and built a table-top device that can detect residual BCC on the resection surface of removed skin tissue specimens by using a combined Raman spectroscopy and auto-fluorescence microscopy approach. The Fast Raman device can measure the excision surface of skin specimens up to 2x2 cm in 30 minutes. The instrument is automated and highlights the presence of BCC objectively, enabling its use by clinical staff without extensive training. 

The Fast Raman device was recently integrated into the Mohs clinical workflow at the Nottingham NHS Treatment Centre and was used to obtain proof-of-concept results by measuring fresh tissue specimens intra-operatively. We measured a total of 115 fresh skin tissue layers (from 113 patients) from head-and-neck area, including nose, temple, eyelid, cheek, forehead, eyebrow, and lip. These measurements have provided an indication of instrument performance and have highlighted some of the challenges of translating such spectroscopic techniques from the laboratory into the clinic.

Speaker: Dr Sebastian Skatulla

Zoom link: https://uofglasgow.zoom.us/j/93968561981?pwd=aHliYjd4bHNhN2pib1pLU1VNT24yZz09

Title: Micromorphic Tissue Mechanics Accounting For Non-Affine Myocardial Deformation Characteristics 

Abstract: Cardiovascular diseases are among the most common causes of death in the world. Computational modelling in combination with medical imaging techniques, mechanical tissue testing, as well as cell and molecular biological analysis has the potential to help better understanding the underlying physiological mechanisms of heart failure and guide decision making in finding patient-specific treatment options in the future.

Computational models, however, need to be realistic enough to accurately describe the highly heterogeneous and non-uniform myocardial material composition [1], its anisotropic mechanical properties, the electro-mechanical interaction during muscle contraction and other biological effects, such as residual stresses and remodelling processes.

In this contribution we want to focus on the passive response of the myocardium which is very compliant exhibiting large strains, in particular, while the heart is contracting and twisting to eject oxygenated blood into the circulatory system during the systolic phase of the heart cycle. In the past it has been discovered that the initially crimped and coiled collagen fibres straighten during passive filling [2] and that cardiac myocytes exhibit a certain degree of motion flexibility within the constraining cytoskeleton [3, 4]. In contrast to classical models of phenomenological nature, this work proposes a micromorphic continuum-based formulation [5] which features extra degrees of freedom and corresponding strain and stress measures. The approach can therefore account for the hierarchical fibrous characteristics of the myocardium which are associated with micro-structural deformation of muscle-fibre bundles as well as their motion relative to the bulk material. As such, the assumed hyperelastic material behaviour of myocardial tissue is represented by a non-linear strain energy function which includes contributions linked to the bulk material representing the cytoskeleton and the micromorphic-fibre continuum emulating the micro-kinematics of the interwoven muscle-fibre bundles.

Short Bio: Sebastian Skatulla is an Associate Professor of Structural Engineering and Mechanics at the University of Cape Town. He graduated  as Diplom Bau-Ingenieur (TH) from the Karlsruhe Institute of  Technology (KIT) in 2003. He was awarded his PhD degree in Mechanical Engineering from the University of Adelaide in 2007.

He is the Director of the Computational Continuum Mechanics Research Group (CCM)  which has its research activities centred in multiscale and multiphase continuum methods. Current activities comprise the poroelasticity of Antarctic sea-ice and biological tissue.

He is the President of the South African Association for Theoretical and Applied Mechanics (SAAM) and member of the Scientific Council of the International Centre for Mechanical Sciences (CISM).

References

[1] LeGrice, I.J., Smaill, B.H., Chai, L.Z., Edgar, S.G., Gavin, J.B. and Hunter, P.J., Laminar structure of the heart: ventricular myocyte arrangement and connective tissue architecture in the dog. Am. J. Physiol. Heart Circ. Physiol., 269:H571–H582, 1995.

[2] Robinson, T.F., Geraci, M.A., Sonnenblick, E.H. and Factor, S.M., Coiled perimysial fibers of papillary muscle in rat heart: Morphology, distribution, and changes in configuration. Circulation Research, 63:577–592, 1988.

[3] LeGrice, I.J., Takayama, Y. and Covell, J.W., Transverse shear along myocardial cleavage planes provides a mechanism for normal systolic wall thickening. Circulation research, 77(1):182–193, 1995.

[4] Spotnitz, H.M., Spotnitz, W.D., Cottrell, T.S., Spiro, D. and Sonnenblick, E.H., Cellular basis for volume related wall thickness changes in the rat left ventricle. Journal of molecular and cellular cardiology, 6(4):317–331, 1974.

[5] von Hoegen, M., Skatulla, S. and Schrder, J., , ”A generalized micromorphic approach accounting for variation and dispersion of preferred material directions”, Computers and Structures, 232: 105888, 2020

Speaker: Florentin Wörgötter

https://uofglasgow.zoom.us/j/93893720657

Modern AI is currently one of the central catch phrase in society and politicians consider it a “disruptive technology” potentially leading to major changes in the world, because – yes – there are indeed extremely powerful applications existing. Therefore, some handle this like the modern revelation for saving all our souls. In this talk, I would like to adopt a more critical stance and start by discussing “what scientist should not do (anymore)”, when it comes to Deep Learning research. 

Following this, I will try to show some examples from our own work, with the hope to convince the audience that we “do it (at least a little bit) better”. Specifically, first we show how across-layers feedback in networks will massively improve their performance. Second, we demonstrate, how certain NN-structures can be used to improve chaotic time series prediction by several 100%. And, third, we provide an algorithm to solve planning in complex mazes. This is traditionally addressed iteratively using step-after-step calculations. Different from this we can do this in a single shot with networks that do not have to learn anything. Strangely, this would allow Uber to calculate the shortest route for several taxis to reach a customer in one go, without having trained the system on the multi-agent problem.

From all this, my personal summary concerning Deep Learning is that it becomes more and more important to search for relevant questions than just to address more and more application examples. 

Biography: Florentin Wörgötter studied biology and mathematics at the University of Düsseldorf, Germany. He received a Ph.D. degree, studying the visual cortex, from the University of Essen, Germany, in 1988. From 1988 to 1990, he was engaged in computational issues with the California Institute of Technology, Pasadena. He was a Researcher with the University of Bochum, Germany, in 1990, where he was investigating experimental and computational neuroscience of the visual system. From 2000 to 2005, he was a Professor of computational neuroscience with the Psychology Department, University of Stirling, U.K., where his interests strongly turned towards “Learning in Neurons.” Since July 2005, he has been the Head of the Computational Neuroscience Department at the Bernstein Center for Computational Neuroscience, Inst. Physics 3, University of Göttingen, Germany. His current research interests include information processing in closed-loop perception–action systems, especially addressing early cognitive aspects as well as learning/plasticity, which are tested in different robotic implementations. His group had developed the RunBot, which in its time about 12 years ago had been for quite a while the fastes, dynamic and adaptive biped walking robot based on neural control. 

Speaker: Dr Silvia Caponi

Special seminar, hosted by Massimo. 

Speaker: Ryo Torii

Bio: Ryo Torii is an associate professor at UCL Mechanical Engineering. His research interests have been in both developing new techniques of computational modelling and application to clinically-relevant problems, which are working hand-in-hand. Recent applications are primarily in the area of coronary artery atherosclerosis, especially to predict disease progression and to evaluate performance of various types of stents using computational modelling. He has been promoting use of computational modelling in clinical studies and has recently published an expert recommendation paper for clinical community (Gijsen et al. European Heart Journal 2019). He also has activities outside coronary artery pathophysiological analyses, including biomechanical analysis of aortic valve disease, growth prediction of tissue-engineered muscular constructs and process optimisation of biofabrication systems.

Speaker: Ruri Hidema

The seminar related to dilute solution rheology and microfluidics will be composed of two parts.

The first part is the observation of sodium hyaluronate (Hyaluronic Acid Sodium salt, Na-HA) solution in planar abrupt contraction-expansion microchannels to discuss the effects of polymer flexibility and entanglement on elastic instability. As the rigidity of Na-HA depends on the ionic strength of a solvent, Na-HA was dissolved in water and phosphate buffered saline. The flow regimes of the Na-HA solutions in several planar abrupt contractionexpansion channels were characterized by rheological properties of the solution. It was found that the entanglement of Na-HA in the solution is a more dominant factor affecting the flow regimes than the solution relaxation time and polymer rigidity [1].

The second part of the seminar is measurements of drag force due to synthetic polymers in flowing fluids by using a scanning probe microscope (SPM). Methoxy polyethyleneglycol thiol (mPEG-SH) was attached to the cantilever probe of the SPM, which was further immersed in flows of glycerol and polyethyleneglycol (PEG) solutions. The mPEG-SH-bonded cantilever detects the extra force due to polymer-polymer and polymer-fluids interaction in flowing fluids. The conformation of the mPEG-SH polymer bonded to the probe of the cantilever was predicted, and the drag force due to the deformed mPEG-SH was calculated. The forces detected by experiments using the SPM and the forces obtained by model calculations were compared, and found to be reasonably close [2].

Speaker: Alexander Liddle

DNA self-assembly provides a route to scalable nanostructure fabrication with molecular precision.  It can be used to build functional, dynamic devices incorporating a wide variety of other nanoscale objects.  However, to fully realize the versatility of this technology requires that the rules that link yield to design and processing must be elucidated.  DNA origami, in which a long single strand of DNA is folded into a predetermined structure by the addition of ≈ 200 oligomers that bind sequence-specifically, is a common and representative system.  We are working to understand the thermodynamics and kinetics that govern nanostructure assembly – a task that is complicated by the high level of cooperativity that occurs during the origami folding process

Speaker: Professor Dietmar Hutmacher

Prof Hutmacher is a biomedical engineer, an educator, an inventor, and a creator of new intellectual property opportunities. He directs the Centre for Regenerative Medicine and the ARC Training Centre in Additive Biomanufacturing at QUT, a multidisciplinary team of researchers including engineers, cell biologists, polymer chemists, clinicians, and veterinary surgeons. Prof Hutmacher is an internationally recognized leader in the fields of biomaterials, tissue engineering and regenerative medicine with expertise in commercialization. He has translated a bone tissue engineering concept from the laboratory through to clinical application involving in vitroexperiments, in vivo preclinical animal studies and ultimately clinical trials. His recent research efforts have resulted in traditional scientific/academic outputs as well as pivotal commercialisation outcomes. His pre-eminent international standing and impact on the field are illustrated by his publication record (more than 300 journal articles, edited 14 books, 70 book chapters and some 500 conference papers) and citation record (google scholar, more than  44,000 citations, h-index of 104 ).

Speaker: Pavel Leinveber

• 5 pm         Registration
• 5.30 pm    Evening Lectures - Yudowitz Lecture Room, Wolfson Medical Building
  • A Welcome form University of Glasgow (10 minutes)
  • An Introduction to FNUSA-ICRC, Brno (home of Mendel & genetics) (20 minutes)
  • “Innovations in Electro-cardiology for Diagnosis & Management of Heart Failure” Pavel Leinveber (30 minutes)

• Questions: (15 minutes)

Speaker: Prof. Zulfiqar Ali

Zulfiqur has a first degree in Chemistry and a PhD in Instrumentation and Analytical Science from the University of Manchester. He was research fellow at the University of Warwick carrying development of an amperometric glucose sensor for diabetes monitoring. Zulfiqur has held academic positions in the Department of Pharmacy at the University of Brighton and the School of Science and Engineering at Teesside University. He has been Assistant Dean for Research and Innovation as well as Dean of the Graduate Research School at Teesside University where he had responsibility for the University’s REF 2014 submission. He now has responsibility for the Healthcare Innovation Centre (HIC) which a partnership with TWI Ltd. Zulfiqur has research interests within novel electrochemical and optical transduction, micro and nanofabrication, microfluidics, bioprocessing and point-of-care testing. He is also director of Anasyst Ltd which is a spin-out company that has arisen from some of his research.

Speaker: Heleen Fehervary

Speaker: Professor Mark Jensen

Speaker: Prof Qun Fang

We developed a sequential operation droplet array (SODA) technique for performing automated picoliter to nanoliter-scale droplet manipulation, analysis and screening. It can achieve multiple liquid handling manipulations including droplet generation, indexing, transferring, splitting and fusion, under control of a computer program. We have applied the SODA systems in multiple areas including enzyme inhibitor screening, cell-based drug combination screening, protein crystallization screening, digital PCR, cell culture and migration testing, single cell microRNA quantification and single cell proteomic analysis. 

Host Thomas Franke

Speaker: Dr Samuel Wall

Biophysical models describing the function of the cardiovascular system promises improved understanding of the heart and its dynamics in health and disease.   However, the myocardium is a complex multiscale material, and while detailed models and constitutive laws have been developed to describe its behaviour, fitting these models meaningfully to actual data is often complicated by the large number, and the interaction, of required parameters.  Although numerous techniques, from trial and error to advanced optimization, can be used to fit data, challenges still exist, often due to the computational requirements when many parameters need to be varied.    This is particularly difficult when considering real observations, where heterogeneous noisy data sets are the norm, and often time constraints not compatible with long computational requirements.   Here we discuss a range of simulation approaches linked to such experimental and clinical data streams, where we perform rapid data assimilation in order to increase the information content of measurements.  Examples include creating new diagnostics for clinicians, or the ability to quantitate the molecular effect of tested drugs on key cellular pathways.  These data driven approaches are enabled by modern techniques in software and hardware that allow rapid solving of difficult optimization problems.  We consider cases from whole organs to engineered microtissues, with a unified goal of integrating in silico computational frameworks into measurement streams to improve the resolution of mechanistic understanding in complex systems.

Organiser - Nikolaj Gaadegard

Speaker: Dr Thomas Iskratsch

Investigating Cardiomyocyte Mechanosensing with Nanopillars and Nanopattern

The composition and the stiffness of cardiac microenvironment change during development and/or in heart disease. Cardiomyocytes (CMs) and their progenitors sense these changes, which decides over the cell fate and can trigger CM (progenitor) proliferation, differentiation, de-differentiation or death. The field of mechanobiology has seen a constant increase in output that also includes a wealth of new studies specific to cardiac or cardiomyocyte mechanosensing. As a result, mechanosensing and transduction in the heart is increasingly being recognized as a main driver of regulating the heart formation and function. However, the molecular mechanism of cardiomyocyte rigidity sensing is still elusive.

To study the regulation of cardiomyocyte rigidity sensing on a molecular level we combine nanopillar arrays, PDMS gels with defined stiffness and FRET molecular tension sensors (Pandey et, Dev Cell, 2018). Moreover, because not only the stiffness but also the molecular composition of the adhesions change in pathological conditions (Ward et al, BBAMCR, 2019) we further want to study the implication of the changing adhesion structure and mechanics in detail. To this aim, we adapted a surface functionalization approach using DNA origami with conjugated receptor ligands (uni- or multivalent) that are placed onto nanopatterns fabricated with electron beam lithography (Hawkes et al, Faraday Discussions, 2019).

Together our approach indicates a specific cardiomyocyte rigidity sensing mechanism and gives new insights into the nanoscale organisation of cardiomyocyte integrins.

Speaker: EMBL-EBI trainers

The Ensembl project at www.ensemblgenomes.org can also be covered if participants are working with bacteria, plants, fungi, protists or (invertebrate) metazoa.

Aimed at wet-lab researcheres, the only prerequisite for the browser workshop is general knowledge of molecular biology and genomics and a familiarity with web browsers.

Only £60. 

More information here: https://www.polyomics.gla.ac.uk/course-ensembl-workshop_Apr_2019.html

Speaker: Sanjay Pant

Patient-specific computational modelling of biomedical systems requires estimation of a large number of model parameters. This estimation is typically performed through clinical measurements, which are inherently uncertain, acquired in the patient. In recent years Kalman-filtering type methods, which can be viewed as recursive Bayesian methods or data assimilation techniques, have gained popularity for this purpose. This talk will present the formulation and application of such methods. Particular focus will on the the widely used Unscented Kalman Filter, which provides computational efficiency. Applications will be presented on both lumped-parameter and geometric multi-scale models of haemodynamics. Two pathophysiologies of congenital heart disease will be of concern: i) coarctation of the aorta; and ii) hypoplastic left heart syndrome (single-ventricle physiology). Finally, some aspects of parameter identifiability, the question of whether parameters can be identified/estimated given a set of measurements, will be presented.

Speaker: Claudio Capelli

Patient-specific computational models have been extensively developed over the last decade and applied to investigate a wide range of the cardiovascular mechanics. Modelling can also offer support to personalized and predictive medicine. Such vision could be particularly suitable to face the wide variety of heart disease. The translation of these technologies into clinical applications, however, is still far from becoming a standard of care in clinical practice and currently limited to few single cases. This talk reports the experience of a single clinical and engineering centre, based in the main UK children hospital, which has been involved in the development of a modelling framework that allows the use of realistic simulations to prospectively support clinical decisions.

After introducing the numerical methods used in this study including finite element analyses (FEA), computational fluid-dynamics (CFD) and fluid-structure interaction (FSI), I will focus on a cohort of patients who were referred for various intervention and how the planning was supported by integrating computational simulations within a clinical workflow. Image data routinely acquired for clinical assessment (MRI, CT, echocardiography, x-ray) were postprocessed to set up patient-specific models. FEA and CFD were performed to predict structural and haemodynamic changes following the procedures. Simulations were carried out to: select the best-matching device for each anatomy; address the risks of spatial interference with surrounding structures; optimize size and positions of device; design a surgical patch. The results were presented during clinical unit’s multidisciplinary meeting. Measurable clinical outcomes from the real procedures were compared with the computer model predictions.

The numerical results of FEA and CFD analyses were in accordance with the delivered treatment in all cases except in one case. Post-procedural images were also used to confirm correct prediction of sizing and positioning of the stent. Pressure and velocity data acquired by transthoracic echocardiography showed agreement with the results calculated with CFD analyses with a max error less than 3 mmHg. Each computational framework process was completed within a week with no requirements for additional clinical data.

The early results of using computer simulations in clinics seem to be promising in terms of reliability of the simulations, response time, and usefulness in clinical practice. The translation of these technologies is crucial as it can limit the procedural risks for treatment of CHD cases.

Speaker: William Peveler

Colloidal (nano)materials offer a huge range of functionality derived by tuning their size, shape and chemical makeup, as well as their interface with the surrounding environment. I will describe several pieces of my work that utilise chemical control over these properties to solve specific biomedical challenges. In particular, I will describe new tools for fluorescent imaging, labelling and sensing derived from biotargetting interfaces on colloidal gold nanoparticles (spheres, rods and clusters) and quantum dots. I will then present recent work on the use of fluorescent polymers as a cross-reactive array-based sensor for liver fibrosis, and discuss the implications of this approach for biomarker detection and discovery. 

References:

(1)        Peveler, W. J.; Yazdani, M.; Rotello, V. M. ACS Sens.2016, 1(11), 1282.

(2)        Cortés, E.; Huidobro, P. A.; Sinclair, H. G.; Guldbrand, S.; Peveler, W. J.; Davies, T.; Parrinello, S.; Görlitz, F.; Dunsby, C.; Neil, M. A. A.; Sivan, Y.; Parkin, I. P.; French, P. M. W.; Maier, S. A. ACS Nano2016, 10(11), 10454.

(3)        Peveler, W. J.; Landis, R. F.; Yazdani, M.; Day, J. W.; Modi, R.; Carmalt, C. J.; Rosenberg, W. M.; Rotello, V. M. Adv. Mater.2018, 30(28), 1800634.

(4)        Algar, W. R.; Jeen, T.; Massey, M.; Peveler, W. J.; Asselin, J. Langmuir2018, 10.1021/acs.langmuir.8b02733.

Biography:

Dr William Peveler is a University of Glasgow Lord Kelvin Adam Smith (LKAS) Fellow working between the Division of Biomedical Engineering and School of Chemistry. 

After work with Dr Martin Grossel and Professor Harry Anderson FRS at the University of Oxford for his MChem, Dr Peveler moved to London to undertake a PhD in Chemistry at UCL, as part of the SECReT CDT. His research focussed on colloidal nanomaterials for array-based sensing applications, for which he was awarded the Ramsay Medal.

Dr Peveler then won an EPSRC Doctoral Prize Fellowship and Royal Society International Exchange Grant to undertake 2 years of postdoctoral work with Professors Claire Carmalt and William Rosenberg (UCL/Royal Free Hospital) and Professor Vincent Rotello (UMass Amherst). Here he sought to apply array-based sensing to the problem of liver disease. Most recently Dr Peveler moved to Canada to take up a Killam Postdoctoral Research Fellowship at the University of British Columbia, working on point-of-care diagnostic technologies the laboratory of Professor Russ Algar. Dr Peveler then returned to the UK and moved north to join the University of Glasgow in November 2018, beginning his independent research career.  

Speaker: Professor Alvaro Mata

Speaker: Robert McMeeking

Speaker: Michael Sheetz

Speaker: Albert Folch

Digital Manufacturing (DM) - of which 3D-Printing is an example - has been applied with great success to improve design efficiency and part performance in the automobile industry, aeronautics, microelectronics, architecture, sportswear, and biomedical implants, among others. However, by comparison with other manufacturing fields, microfluidics has been slow to adopt DM. Microfluidic chips are still designed largely from scratch, the materials (usually thermoset or thermoplastic polymers) are often manually poured into a mold to form 2D-layer replicas, and the mold replicas are manually aligned and