Find research datasets worth reusing

Artificial intelligence for identification of blood cells - Prof Huiyu Zhou (University of Leicester)

This video is the eleventh talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 14/09/2022. Artificial intelligence for identification of blood cells - Prof Huiyu Zhou (University of Leicester) Bio: Prof. Huiyu Zhou received a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou currently is a full Professor at School of Computing and Mathematical Sciences, University of Leicester, United Kingdom. He has published over 400 peer-reviewed papers in the field. He was the recipient of "CVIU 2012 Most Cited Paper Award", “MIUA 2020 Best Paper Award”, “ICPRAM 2016 Best Paper Award” and was nominated for “ICPRAM 2017 Best Student Paper Award” and "MBEC 2006 Nightingale Prize". His research work has been or is being supported by UK EPSRC, ESRC, AHRC, MRC, EU, Royal Society, Leverhulme Trust, Puffin Trust, Alzheimer’s Research UK, Invest NI and industry. Homepage: https://le.ac.uk/people/huiyu-zhou. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/N5AjIUAwYp4

"Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading)

This video is the tenth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 14/09/2022. "Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading) Bio: Al Edwards has a background in fundamental immunology combined with expertise in biochemical engineering, he is an interdisciplinary researcher focussed on solving current and future healthcare challenges using an engineering science approach that combines a range of fields from biology, biochemistry, chemistry and physics. He works at the interface between academic technology discovery and industrial development and have experience of both fundamental research and the commercialisation of new technology. The two main challenges he currently works on are the development of affordable microfluidics for clinical diagnostics and microbiology, and the engineering science of complex biologic therapeutics such as vaccines. Alexander's research is funded from a wide range of sources, including NIHR , EPSRC, SBRI Healthcare, the Wellcome Trust, Innovate UK and industry Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/21a78Vql8b0

Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)

This video is the seventh talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University) Bio: Bryant Lin, MD, MEng is a primary care physician, educator and researcher. The cornerstone of Dr. Lin's work is keeping medicine focused on humans - patients, providers, families and trainees - and not lost in technology and algorithms. His research and educational interests span (1) Developing and testing novel medical technologies, (2) Improving the health of Asian populations with Precision and Population Health, and (3) Increasing expression and interconnections in the Health Community with the Humanities and Arts. After receiving his undergraduate and master's degrees in Electrical Engineering and Computer Science from MIT, he completed his MD and internal Medicine training at Tufts University School of Medicine and Tufts Medical Center. He came to Stanford to serve as a Research Fellow in Cardiac Electrophysiology and Biodesign Fellow where he learned to identify unmet human-centered needs. Since completing his post-graduate training, he stayed at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine where he has invented and researched new medical technologies addressing unmet human-centered needs and started the Consultative Medicine Clinic evaluating patients with medical mysteries. He serves as the Training Director for the Joe and Linda Chlapaty DECIDE Center which has created a novel shared decision making tool for atrial fibrillation anticoagulation and is an investigator in several active clinical trials. Three years ago, he co-founded and currently co-directs, with Dr. Latha Palaniappan, the Center for Asian Health Research and Education (CARE) which aims to improve the health of Asians everywhere. Most recently, he has worked closely with the Medicine and the Muse leadership to help start the Stuck@Home concert series, the Stanford SoundWalk and the COVID Remembrance project. Dr. Lin has an active interest in storytelling and film-making. He co-directs an undergraduate seminar, MED 53Q “Storytelling in Medicine”, with Dr. Lauren Edwards and is working with a group of students on a documentary on end-of-life care at a JapaneseAmerican Senior Home in the Bay Area. Kevin Chang MD, MS, is a primary care physician. His focus in on patient care, population health and quality improvement, and medical education. He received his undergraduate degree and master's degree in biomedical engineering from Duke University and Stanford University respectively, and completed his MD at New York University, followed by his medical training at Stanford University. He has since stayed on at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine, where he also serves as the co-director of the resident physician Internal Medicine clinic. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/ozk1iJYC1yk

Transforming the UK's diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick)

This video is the second talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Transforming the UK’s diagnostics agenda after COVID-19 and grand challenges – Future Blood Testing Landscape report - Prof Dimitris Grammatopoulos (University Hospitals Coventry & Warwickshire, University of Warwick) Bio: Dimitris Grammatopoulos, PhD, FRCPath, is Professor of Molecular Medicine at Warwick Medical School and Consultant in Clinical Biochemistry and Molecular Diagnostics at the University Hospitals of Coventry and Warwickshire, NHS Trust, United Kingdom. He also leads the Novel Biomarkers theme of the Institute of Precision Diagnostics and Translational Medicine, Pathology-UHCW NHS Trust. where he combines clinical expertise in diagnostic laboratory medicine with a research track-record in application of cutting edge multidiscipline methodologies in routine clinical diagnostics. He received academic and clinical training in Newcastle, Bristol, Johns Hopkins-Baltimore and Warwick. He has expertise in biochemical/molecular diagnosis of many endocrine and metabolic disorders. His translational research interest is focused on stress hormones and homeostatic adaptations of fetal development to maternal disease as well as development of novel -omics based biomarker approaches suitable for precision medicine and better characterisation of patient phenotype. He has experience around use of AI and ML for development and refinement of clinical and diagnostic pathways for complex chronic conditions that are considered as national priorities. Dimitris is the Lead in Diagnostics, Global Health Priorities in Health, University of Warwick. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/HiOlRzJPR7Q

Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).

This video is the first talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, University of Reading). Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/I1xsig9C8w0

Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading)

This video is the fifth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Remote blood monitoring for cancer patients- a preliminary landscape analysis - Beth Harvey (University of Reading) Bio: Beth Harvey is currently a master’s student in digital health and data analytics at the Henley Business School. Having completed her bachelor’s in biomedical science, Beth has then gone on to work in the medical device and IVD regulatory sector with a consultancy firm in Vancouver. After moving back to the UK in 2019 Beth worked with multiple UK manufacturers in the digital health space which spurred her interest in the field she is now studying. Beth’s research interests are in healthcare innovation remote patient monitoring, and data analysis. She is currently finishing her dissertation on the opportunities and challenges for remote blood monitoring in oncology in collaboration with the Future Blood Network. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/8pbO3N9IQKM

opencc-by-4.0Nov 2021View details →

Network Theme: The potential of machine learning and AI for blood based investigations - Professor Jeremy Frey (University of Southampton)

This video is the sixth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Network Theme: The potential of machine learning and AI for blood based investigations - Professor Jeremy Frey (University of Southampton) Bio: Prof Jeremy Frey Professor of Physical Chemistry, Head of Computational Systems Chemistry, University of Southampton (UoS). He is PI of AI for Scientific Discovery Network+, and co_I on the Internet of Food Things Digital Economy Network+ and has had considerable involvement in the UK e-Science and Digital Economy programmes for many years (e.g., PI of the Digital Economy IT as a Utility Network+. He is a strong proponent of interdisciplinary research and the use of digital technology and ideas to enhance methods of scientific research & development. His own research involves activities across the physical land life sciences, from the application of novel mathematical analysis (e.g., Topological Data Analysis), laser spectroscopy and imagining techniques to chemical and biological problems, with the development of sensors and imagining systems such as the novel soft x-ray microscope. In parallel he works on the integration of these techniques with full provenance environment into laboratory systems using semantic web technologies. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/eNORwfMy5cE

opencc-by-4.0Nov 2021View details →

The Application of Machine Learning for Classification on Blood Pressure Variability. A New Approach for an Old Idea - Professor Kelvin Tsoi (The Chinese University of Hong Kong, School of Public Health and Primary Care)

This video is the eighth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. The Application of Machine Learning for Classification on Blood Pressure Variability. A New Approach for an Old Idea - Professor Kelvin Tsoi (The Chinese University of Hong Kong, School of Public Health and Primary Care) Bio: Professor Kelvin Tsoi is an Epidemiologist specialized in Digital Health. His research interests focus on digital innovation in chronic disease management, including mobile and telecare application for hypertension management, technological implementation and social engagement for cognitive screening, artificial intelligent application on electronic health records. He also works as the traditional epidemiologist on evidence-based medicine and population cohort studies. He obtained his Bachler Degree from Department of Statistics and Doctor of Philosophy from School of Public Health in the Chinese University of Hong Kong. He further received post-doctoral training in the Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics. He was also appointed as a Director of CUHK JC Bowel Cancer Education Centre to promote colorectal cancer screening. In 2011, he worked as a research scientist in Hospital Authority. He led projects covering a wide range of service areas on chronic diseases, such as service demand projection for schizophrenia and dementia. The experience of database management enhanced his understanding of the HA database structures. In 2013, he was invited to join the interdisciplinary team for Big Data research and worked closely with a team of engineers and data scientists. Currently, Professor Tsoi is an Associate Professor in JC School of Public Health and Primary Care, SH big Data Decision Analytics Research Centre and JC Institute of Ageing. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/liLVKA-JHiI

opencc-by-4.0Nov 2021View details →

Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)

This video is the fourth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick) Bio: <a href="https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1147">Dr Mark Elliott</a> Mark is an Associate Professor at the Institute of Digital Healthcare, WMG, University of Warwick (UoW). Mark’s core research focuses on human movement and physiology analytics. His research uses signal processing and data science approaches to monitor, measure and model human movement and physiology to infer health status. He is the PI of the WMG Motion Capture Laboratory. His work further extends into the broader area of using wearable and on-the- body sensing devices to make objective measures of human behaviour and behaviour change. Much of Dr Elliott’s research is highly applied and involves collaborating with commercial and NHS partners. He has received funding from EPSRC, Innovate UK and SBRI Healthcare, as well as direct industrial funding. He is currently Data Analytics Theme Lead for the EPSRC funded OATech+ Network and on the steering committee for the EPSRC funded VSimulators facilities at Bath and Exeter. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/ChdbggScUgo

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Evaluation of DNA extracted from timber rattlesnake (Cotalus horridus) cloacal and blood swabs for microsatellite genotyping

Genetic research is a key component to modern wildlife conservation, but it is contingent on the collection of reliable and high-quality genetic samples. Invasive genetic sampling techniques have potential to negatively impact individuals, which may be prohibitive when working with threatened and endangered species. Prior to sample collection, project managers must try to balance the negative impact on individuals included in the study with the demand for DNA and the difficulty of obtaining samples. Although established methods for blood and tissue collection in reptiles meet the need for high-quantity and quality DNA, they inherently require longer handling times and more skill to obtain. Thus, non-invasive DNA collection methods, such as cloacal swabs, may be preferred when animal welfare is a priority. Cloacal swabs are quicker, easier, require less training and reduce handling time. To evaluate cloacal swabbing as an alternative to collecting blood, we obtained both cloacal and blood swabs. We extracted DNA from cloacal and blood cells that were collected from 23 Timber Rattlesnakes (Crotalus horridus). We assessed DNA by purity (A260/A280), concentration, and microsatellite genotyping. Our results show high-quality DNA can be obtained from both cloacal swabs and blood samples, but quality and concentration of DNA was significantly lower from cloacal swabs. Further, degradation and contamination affects the performance of cloacal DNA when compared to blood DNA in microsatellite-based genotyping. Although we recommend collecting blood samples whenever possible to obtain the highest-quality DNA, cloacal swabs represent a viable alternative for genetic sampling when using microsatellite loci as genetic markers.

opencc-zeroOct 2022View details →

opencc-by-4.0Mar 2022View details →

Database that contains all images (plus 180 more) employed in the article: "Image features for quality analysis of thick blood smears employed in malaria diagnosis"

We share with you a bank of images obtained from microscopic fields of thick blood smears employed in the malaria diagnosis, and also the .csv file that contains the labels for each image. The images are saved with a unique name that is found in the first column of the .csv file. The second column contains the labels from each image, according to their unique names. The labeling process was done with the online toolbox Labelbox. Labelbox, "Labelbox," Online, 2020. [Online]. Available: https://labelbox.com  If you are interested in using our database, cite our article as a way to recognize our work. We will be grateful for that.  CITATION: Fong Amaris, W.M., Martinez, C., Cortés-Cortés, L.J. et al. Image features for quality analysis of thick blood smears employed in malaria diagnosis. Malar J 21, 74 (2022). https://doi.org/10.1186/s12936-022-04064-2 URL of our paper: https://malariajournal.biomedcentral.com/articles/10.1186/s12936-022-04064-2 --- This is the link where you can find our images Bank: https://drive.google.com/drive/folders/1Qrv0e4bSEtkeqtPABz-klQp-6D6OjU-X?usp=sharing  It is important you to know that along with this .txt file, we are sharing the .csv file that contains 600 names of images (in the first column) with their respective labels (second column aside) This file corresponds to the instructions of an extended label file related to 600 images (and 600 new labels) in contrast to our previous label file with 420 labels from 420 images (https://www.researchgate.net/publication/359439520_Database420LabelsInstructionstxt ; https://www.researchgate.net/publication/359438904_Database420Labelscsv). Best Regards

opencc-by-4.0Oct 2017View details →

Serum albumin domain structures in human blood serum by mass spectrometry and computational biology

Contact prediction data generated by EPC-map used in the paper "Serum Albumin Domain Structures in Human Blood Serum by Mass Spectrometry and Computational Biology" by Rappsilber et al.

Fig. 9 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 9 Overall framework of proposed automated malaria diagnosis and species identification. CNN, Convolutional neural network; RBC, red blood cell; YOLO, You Only Look Once (model)

Fig. 8 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 8 Examples of false positive predictions by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)

Fig. 6 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 6 Comparison of detection performance by the original YOLOv4 model and the YOLOv4-RC3_4 model. Red arrows indicate cells not detected by the original YOLOv4 model, green arrows indicate the same cells detected by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)

Fig. 3 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 3 Network structure of YOLOv4. CSP, cross-spatial connection; SPP, spatial pyramid pooling layer; PANet, Path Aggregation Network; CBM, Convolutional, Batch Normalisation, and Activation; CBL, Convolutional, Batch normalisation, and Leaky-ReLU; Conv, convolutional; Concat, concatenation

Fig. 4 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 4 Building blocks of the residual learning module. CBM, Convolutional, Batch normalisation and Mish (modules)

Fig. 5 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 5 Visual representation of the removal of residual blocks from C3 and C4 Res-block body. YOLO,You Only Look Once (model)

Fig. 1 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 1 Comparison of malaria diagnosis using deep learning CNN models and deep learning object detectors. CNN, Convolutional neural network

Fig. 2 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 2 Cropping of infected cells using the coordinates of predictions by the object detectors. RBC, Red blood cell; YOLO,You Only Look Once (model)