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How to ace the Research Project in SACE

As daunting as it may sound, let’s dive into what the SACE research project is and how you can make it as useful to you as possible (and maybe even fun!)

2 years ago   •   4 min read

Ahhh the research project - the subject of developing a specific, (but not too specific!) open-ended research question on the topic of your choice. Your entire semester will revolve around this developed question, and you’ll keep on coming back to it to write reflections on your progress as you go. As daunting as this may sound, let’s first dive into what the research project is and how you can make it as useful to you as possible (and maybe even fun!)

So what is the research project?

Unlike your other SACE stage 2 subjects being 20 credits, the research project is a 10-credit SACE subject you will either complete in year 11 or 12 depending on which high school you attend. The subject consists of three parts: the folio, outcome, and review for research project A or the evaluation if you are undertaking research project B. Despite research projects A and B having different performance standards, both encourage you to explore a topic of choice in depth, gathering various sources and writing reflections on your learning. In the first few weeks of the subject, your teacher will guide you when developing your question. The folio is 10 pages in length and typically consists of your reflections and the main sources you have collected through your research (both primary and secondary sources!). You will then write an outcome that is essentially answering your original research question. Lastly, comes the evaluation or review where you will write an overall reflection and evaluate the findings in the outcome.

So why is the research project necessary?

While the big workload can be overwhelming at first, the research project is good at teaching you analytical and research skills. Doing source analysis enables you to critically evaluate your chosen sources. You will scrutinize the reliability, credibility, and validity of each of your sources. While the relevance of doing all these analyses may be hard to see at this time, the skills you develop are extremely useful during university and in the workplace. You want to be confident that the information you use can be relied upon and is not something just made up by someone. Treat the research project as a practice for your post-high school life. You want to make sure that you have these skills in your toolbox for when you really need them!

How do I develop the best question for my topic of interest?

The most important part of the research is picking the right topic. You want to pick something you have a strong interest in. This way, it will be much easier for you to feel more motivated to sit down and do your research. However, at the same time, you want to pick a topic that will have lots of research behind it, you don't want to be stuck for sources! To avoid this, write down a list of topics you have an interest in and do some research on each - see what is available online or at a local library. This way, you will be more prepared when your teacher comes over to your desk to ask you what you have done so far! Once you have picked your topic, create another list of possible questions you could investigate. These questions should be open-ended, not just with a simple yes or no answer. Keep in mind you will be writing a 1500 to 2000-word answer to this question, so make it a question you can go into complete depth with. Typical questions should be specific and may begin with ‘to what extent’, ‘evaluate’, ‘what’ or ‘how’. For example, if you picked social media as your topic, your question could be ‘to what extent does social media use impact the attention spans of teenagers aged 13-17?’ rather than ‘does social media impact attention spans?’. You may then have to break down your main question into four more guiding questions to help you structure your folio and outcome. For example, ‘how much time do teenagers aged 13-17 spend on social media every day?’. It is important that you keep documentation of this process as you will be displaying it in your folio.

If you're interested in learning how to write the best SHE task - check out this comprehensive guide.

But how do I complete my folio?

The folio is the first assessment of both research projects A and B. There is no right or wrong way to complete it but you do have to follow specific SACE criteria if you want the highest marks. The majority of students start their project with how they came to their question and a reflection on this process. You can then include the main sources you have used with source analysis. Organising interviews with professionals in your topic’s field and sending out surveys really impresses SACE markers as it shows your engagement with the subject. It demonstrates your research skills and independence to create your own data to support your outcome. Your folio should also include a capability statement to show how you have developed in your chosen SACE capability.

What should I write in my outcome? How do I do my evaluation or review?

Your outcome is the synthesis of all your ideas and findings. You can structure it however you want. This may be in the form of a magazine, report, project, video or in any other form which demonstrates all that research you have done. You must clearly conclude your findings and cite your sources. For research project A, the review begins with a 150-word summary of the process and then a 1500-word review follows which focuses on a reflection of your knowledge and skills as well as the quality of your outcome. For research project B, you should also begin with a 150-word summary of the process and then follow with a 1500-word evaluation, critically evaluating your decisions and processes as well as determining the quality of your outcome. Above all, keep in mind that your teacher is there to help you through this process. It is exciting as you begin to come up with an answer to your question. If you need any help during this time, you can find your best local tutor at: https://kisacademics.com/find-a-tutor . SACE tutors understand how stressful it can be and are more than happy to help!

Written by KIS Academics Tutor for SACE English, Biology and Psychology, Charlotte Kenning. Charlotte is currently pursuing a Bachelor of Speech Pathology at Flinders University and has received stellar reviews from her past KIS Academics students. You can view Charlotte's profile here and request her as a tutor.

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Curriculum Guide

Activating Identities & Futures (AIF) A - Stage 1

Course length, compulsory or choice, pre-requisites, course overview.

Stage 2 Activating Identities & Futures (formerly Research Project) is a compulsory 10-credit subject. Students must achieve a  C– grade or better to complete the subject successfully and gain their SACE. 

In Activiating Identities and Futures (formerly Research Project A) students choose a research question that is based on an area of interest. They identify one or more capabilities that are relevant to their research. Students use the research framework as a guide to developing their research and applying knowledge, skills, and ideas specific to their research question. They choose one or more capabilities, explore the concept of the capability or capabilities, and how it or they can be developed in the context of their research.  Students synthesise their key findings to produce a Research Outcome, which is substantiated by evidence and examples from the research. They review the research processes used, and the quality of their Research Outcome.

For Research Project A, students can choose to present their external assessment in written, oral, or multimodal form.   

School Based Assessment •    Folio - 30% •    Research Outcome - 40%

External Assessment •    Evaluation - 30%  

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This course is for Stage 2 students who have not completed Research Project or Activating Identities and Futures.

Activating Identities and Futures is a compulsory element of the SACE which students must complete with a C- or higher grade in order to gain their SACE.

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In this subject, you will have the opportunity to explore ideas related to an area of personal interest and:

• consider the purpose and value of learning for self, others and/or community
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Publications & resources, active implementation frameworks (aif) for successful service delivery: catawba county child wellbeing project, focus area(s): .

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Traditional approaches to disseminating research-based programs and innovations for children and families, which rely on practitioners and policy makers to make sense of research on their own, have been found insufficient. There is growing interest in strategies that “make it happen” by actively building the capacity of service providers to implement innovations with high fidelity and good effect. This article provides an overview of the Active Implementation Frameworks (AIFs), a science-based implementation framework, and describes a case study in child welfare, where the AIF was used to facilitate the implementation of research-based and research-informed practices to improve the well-being of children exiting out of home placement to permanency. In this article, we provide descriptive data that suggest AIF is a promising framework for promoting high-fidelity implementation of both research-based models and innovations through the development of active implementation teams.

Metz, A., Bartley, L., Ball, H., Wilson, D., Naoom, S., & Redmond, P. (2014). Active Implementation Frameworks (AIF) for Successful Service Delivery: Catawba County Child Wellbeing Project. Research on Social Work Practice , 1-8.

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About the AIF

The AIF database is drawn from a range of official sources, including personal files on the National Archives website, and Embarkation and Nominal Rolls on the Australian War Memorial website.

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First AIF Order of Battle

Ross Mallett's First AIF Order of Battle 1914-18 web pages which list the units and formations of the First AIF and where they were raised and served, have been recently restored to the AIF Project website.

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Exploring Identities and Futures (EIF) is an exciting flagship subject that responds to the rapidly changing local and global context that our students are living and learning in. EIF is a Stage 1 subject that supports students to learn more about themselves and explore their aspirations and future. EIF forms the foundations for Activating Identities and Futures (AIF) and is underpinned by the same four pedagogical approaches (agency, natural evidence of learning, self-regulatory learning and feedback). AIF replaces the Research Project.

EIF prepares students for a different way of thinking and learning in senior school. As students begin their SACE journey, they build the knowledge, skills, and capabilities required to be thriving learners and are empowered to take ownership of where their pathway leads, exploring interests, work, travel and/or further learning.

In this compulsory 10-credit subject, which replaces Personal Learning Plan, students must achieve a C grade or better in order to attain their SACE. In 2024 EIF will be in the full implementation phase of the roll out across the state.

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A formal analysis of the AIF in terms of the ASPIC framework

2010, Frontiers in Artificial Intelligence and Applications

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Abstract The theory of argumentation is a rich, interdisciplinary area of research straddling the fields of artificial intelligence, philosophy, communication studies, linguistics, and psychology. In the last years, significant progress has been made in understanding the theoretical properties of different argumentation logics. However, one major barrier to the development and practical deployment of argumentation systems is the lack of a shared, agreed notation or “interchange format” for argumentation and arguments.

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This document presents an overview of some of the standard semantics for formal argumentation, including Dung’s notions of grounded, preferred, complete and stable semantics, as well as newer notions like Caminada’s semi-stable semantics and Dung, Mancarella and Toni’s ideal semantics. These semantics will be treated both in their original extension-based form, as well as in the form of argument labellings. Our treatment includes a sketch of few algorithms for skeptical as well as for the credulous approach to argumentation.

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AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance

Cian m scannell.

School of Biomedical Engineering & Imaging Sciences, King’s College London, 4th Floor Lambeth Wing, St Thomas′ Hospital, London SE1 7EH, UK

Department of Biomedical Engineering, Eindhoven University of Technology, Gemini-Zuid, Groene Loper 5, 5612 Eindhoven, The Netherlands

Ebraham Alskaf

Noor sharrack.

Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Clarendon Way, Leeds LS2 9JT, UK

Reza Razavi

Sebastien ourselin, alistair a young, amedeo chiribiri, associated data.

The derived data and codes to reproduce the results of this study are available at https://github.com/cianmscannell/ai-aif . The original patient data may be made available upon request to the corresponding author.

One of the major challenges in the quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) is the estimation of the arterial input function (AIF). This is due to the non-linear relationship between the concentration of gadolinium and the MR signal, which leads to signal saturation. In this work, we show that a deep learning model can be trained to predict the unsaturated AIF from standard images, using the reference dual-sequence acquisition AIFs (DS-AIFs) for training.

Methods and results

A 1D U-Net was trained, to take the saturated AIF from the standard images as input and predict the unsaturated AIF, using the data from 201 patients from centre 1 and a test set comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 ( n = 44). Fully-automated MBF was compared between the DS-AIF and AI-AIF methods using the Mann–Whitney U test and Bland–Altman analysis. There was no statistical difference between the MBF quantified with the DS-AIF [2.77 mL/min/g (1.08)] and predicted with the AI-AIF (2.79 mL/min/g (1.08), P = 0.33. Bland–Altman analysis shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF (bias of −0.11 mL/min/g). Additionally, the MBF diagnosis classification of the AI-AIF matched the DS-AIF in 669/704 (95%) of myocardial segments.

Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.

Graphical Abstract

Introduction

Stress perfusion cardiac magnetic resonance (CMR) is typically performed with a dynamic contrast-enhanced acquisition in which a bolus of a gadolinium-based contrast agent is visualized passing through the left ventricle (LV) and perfusing the myocardium. This is performed under stress conditions using pharmacologically induced vasodilation to assess areas of inducible hypoperfusion. On the weight of evidence from recent clinical trials, stress perfusion CMR is now one of the guideline-backed methods of choice for the identification of myocardial ischaemia. 1 It has been shown to be highly accurate for the diagnosis of significant coronary artery disease (CAD) and is non-inferior to the invasive reference standard for guiding the management of patients with stable CAD. 2–4 However, Villa et al. 5 showed that the diagnostic accuracy of stress perfusion CMR depends on the level of training of the operator, due to the complexity of visually interpreting the scans. Observer-independent quantitative perfusion analysis may overcome this limitation.

The quantification of myocardial blood flow (MBF), by modelling the tracer-kinetics, represents a viable alternative to visual assessment and can be automated. 6 It, thus, reduces the dependence on the experience of the operator. Additionally, quantitative MBF has been shown to be of independent prognostic value, 7 allow the detection of coronary microvascular dysfunction, 8 and provide insight into ischaemia in a range of other cardiovascular conditions. 9–11 From a technical standpoint, one of the major challenges of MBF quantification is the sampling of the arterial input function (AIF) as required for the tracer-kinetic modelling. The AIF is typically sampled from the basal left ventricular (LV) blood pool or aortic root 12 and because the whole bolus of contrast passes through the LV cavity more-or-less simultaneously, very high concentrations of contrast agent are recorded at the peak of the AIF. There is known to be a non-linear relationship between the concentration of gadolinium and the measured MR signal, particularly at high concentrations and thus, on the standard acquisitions, the measured signal in the LV is saturated. 13

There are potential solutions to the AIF saturation but all have challenges for use in routine clinical practice. For example, a dual-bolus injection of contrast agent, one with a lower dose of gadolinium and hence less saturation, can be used but this adds complexity to the scanning. 14 The dual-bolus increases the potential for errors with the two injections, and has the limitation of measuring the AIF and myocardial tissue curve at different times. Most recent research in quantitative stress perfusion CMR uses a dual-saturation acquisition sequence in which a short saturation time is used to acquire a low-resolution image slice, with reduced signal saturation for AIF estimation, and the myocardium information is subsequently acquired with a standard higher resolution acquisition. 15 , 16 As yet, these dual-saturation methods are not available outside of specialized research settings. The increased imaging time required for the extra AIF slice also means that there is less time available to acquire the standard three slices, potentially leading to reduced spatial resolution or image quality, particularly at high heart rates.

Therefore, there is a clear unmet need for an AIF sampling approach that is both widely available and easy to integrate in clinical routine, ideally with a single-bolus of contrast agent and a commercially available single-saturation sequence acquisition. In this work, we present the artificial intelligence-based AIF (AI-AIF), a deep learning model trained to predict the unsaturated AIF from a saturated single-bolus, single-sequence AIF. This builds on the idea that deep learning models can efficiently learn non-linear mappings, and thus, well-approximate complex physical processes that are otherwise difficult to model, and other recent work on deep learning for parametric mapping, 17 diffusion modelling, 18 and tracer-kinetic modelling. 19 , 20 Specifically, we show that the non-linear mapping from saturated to unsaturated AIF can be learned from a data set of paired saturated and unsaturated AIFs acquired with a dual-saturation sequence. This deep learning-based non-linear correction can then be applied prospectively to the AIF from standard stress perfusion CMR data to allow accurate quantification without the need for a dual-bolus or dual-sequence acquisition, as shown in Figure 1 . It is expected that this will increase the availability of quantitative stress perfusion and make it easier to adapt in clinical practice.

Artificial intelligence-based arterial input function correction: an illustration of the AI-AIF model which takes the saturated AIF from a standard acquisition (left, sampled from the (round) region of interest (ROI) in the LV blood pool) and predicts an unsaturated version which, with the myocardial tissue curve (square ROI), is used in the quantification process to generate a stress MBF map, without the need for any additional input such as from a dual-sequence or dual-bolus acquisition. The neural network model used is a 1-D U-Net model, as is commonly for signal and image processing, which consists of a down-sampling block (encoder) and an up-sampling block (decoder) to reconstruct an output of the same dimensions as the input.

Study population

This was a multicentre retrospective study, with data included from two UK centres [King’s College London (centre 1) and the University of Leeds (centre 2)] which was approved by the institutional research ethics committee and complied with the Declaration of Helsinki. All patients included in this study provided written informed consent, in accordance with the National Research Ethics Service approvals (15/NS/0030 and 18/YH/0168, respectively).

The data used in this study consisted of two parts. The training data set was a retrospective sample of patients exclusively from centre 1, and a test set was comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2. The external dataset, acquired at a different centre, on a different type of MRI system, and at a different magnetic field strength was included to assess the generalization capacity of the model.

Training dataset

The training dataset consisted of a retrospectively collected convenience sample of 201 patients who, between January 2017 and July 2021, underwent cardiac MRI including contrast-enhanced stress perfusion at centre 1 and consented for their anonymized data to be used for research purposes. This dataset was split randomly into 181 and 20 patients for training and model validation, respectively.

Test dataset

The centre 1 test dataset retrospectively enrolled 28 consecutive patients scanned between February 2021 and August 2021, and this was supplemented by 16 patients from centre 2 scanned between March 2021 and September 2021.

Image acquisition

The CMR examinations were performed using two different types of scanning systems. A 3-Tesla (T) Achieva system (Philips Healthcare, Best, the Netherlands) was used at centre 1 and a 1.5-T Ingenia system (Philips Healthcare, Best, the Netherlands) at centre 2.

The gadolinium-enhanced perfusion studies were performed with a saturation recovery spoiled gradient echo sequence with an optimized dual-sequence AIF slice implementation to allow MBF quantification, as previously described. 15 The typical imaging parameters were as follows: repetition time 2.2 ms, echo time 1.0 ms, 100 ms, flip angle 15°, and SENSE acceleration factor 1.8. The low-resolution AIF slice was acquired with the same acquisition parameters except for the short saturation recovery time which was 23.5 ms. In addition to the low-resolution AIF slices, three high-resolution short-axis slices were acquired covering the LV. A bolus of 0.075 mmoL/kg Gadobutrol (Gadovist, Bayer AG, Leverkusen, Germany) was injected intravenously at 4 mL/s using an injector pump (Spectris Solaris, Medrad, Bayer AG), followed by 25 mL of saline flush. Stress perfusion imaging was performed during adenosine-induced hyperaemia (140 µg/kg/min for 3 min, increasing to a further 2 min at 175 µg/kg/min and a further 2 min at 210 µg/kg/min if an insufficient stress response had been achieved).

The AI-AIF uses a deep learning model that is designed to resolve the issue of signal saturation in the AIF for quantitative stress perfusion CMR. As shown in Figure 1 , the deep learning model takes the saturated AIF signal from a standard high-resolution stress perfusion scan and predicts the unsaturated AIF signal without the need for any additional input (like the dual-bolus or dual-sequence). The model is trained with data from dual-sequence acquisitions, acquired at an established centre with extensive experience in quantitative stress perfusion CMR. In particular, the unsaturated data from the short saturation acquisition are used to create a reference standard unsaturated AIF and the network was trained to predict this curve from saturated AIF sampled from the standard acquisition. The final AI-AIF model is made available at https://github.com/cianmscannell/ai-aif , along with the code used for model training.

Model architecture

A 1D U-Net 21 convolutional network (CNN) was employed which consisted of five resolution steps, with each resolution step being comprised of two 1D convolutional blocks with batch normalization, ReLU activations, and dropout (probability = 0.2). 1D max-pooling and transposed convolutions are used for down- and up-sampling, respectively. This model architecture was chosen empirically based on the validation data.

Training details

The model was initialized with He normal weights 22 and was trained for 20 000 iterations (including data augmentation), with a batch size of 10 using the ADAM optimization algorithm (learning rate, 0.001) 23 to minimize the mean squared error (MSE) between the predicted and reference standard unsaturated AIFs. The model with the best validation accuracy was chosen.

Training data

The training database consists of 1D time curves of paired saturated and unsaturated AIFs extracted from dual sequence acquisitions. The pre-injection portion of the AIF is cropped to begin four beats before the arrival of the contract agent in the LV. The values of both the AIFs are normalized by the maximum of the saturated AIF for both training and inference, and can be later correspondingly unnormalized to allow MBF quantification. The curves are cropped or padded to give 64 time points.

Data augmentation

The data augmentation strategy was chosen in order to preserve the quantitative information present in the AIF signal-intensity curves. To achieve this, independent Gaussian noise with zero mean and a standard deviation of 0.02 for the training input and 0.03 for the output was added. This noise was chosen not to substantially impact the signal of the curves but to simulate slightly different realizations of AIF. Furthermore, a random time-offset of 0, 1, 2, or 3 was applied to the starting time of both the input and output to simulate different arrival times of the contrast agent.

MBF quantification

Image analysis.

All image analysis and quantification steps are fully-automated. The stress perfusion images were initially corrected for respiratory motion using a previously described motion compensation scheme, 24 and the segmentation of a ROI for the myocardium was performed using a deep learning-based automated image processing pipeline 6 that detects the right ventricular (RV) insertion points. The AIF is extracted as the average of pixels over an ROI chosen to comprise the pixels greater than the 75th percentile of intensity values within the subendocardial border of the myocardium segmentation, i.e. in the LV blood pool. 25

Quantification

Since the premise of the AI-AIF and DS-AIF approaches is that they correct for the non-linearity of the MR signal with respect to the concentration of gadolinium, the concentration of gadolinium [ C ( t )] can be approximated from the signal intensities ( S ( t )) using a relative signal enhancement conversion 26 :

with the T 1 b of blood taken as 1736ms at 3-T or 1435 ms at 1.5-T and r 1 the contrast agent as 4.5 s -1 mmoL//L ­ ­ , 27 S LV is the signal in the LV blood pool. Quantification of MBF is then undertaken by deconvolving the AIF and myocardial tissue curves on a pixelwise level, using a Fermi function-constrained deconvolution. 28

We evaluated the AI-AIF with respect to the reference standard dual-sequence AIF (DS-AIF) in two stages. In the first stage, the signal-intensity curve of the AI-predicted unsaturated AIF is compared with the DS-AIF. Second, the effect of the differences between the predicted and reference DS-AIFs on the downstream task of stress MBF quantification is assessed.

The median (interquartile range) (IQR) normalized mean squared error (NMSE) between the AI-predicted and reference DS-AIFs is reported. Additionally, the difference in peak values (PV), time-to-peak (TTP), and full width at half maximum (FWHM) of the AIFs is evaluated, and the distributions of PV, TTP, and FWHM of the AI-AIFs are compared with the distributions of PV, TTP, and FWHM of the DS-AIFs using a Mann–Whitney U test, and the distributions are visualized with a boxplot.

Stress MBF is quantified using both the AI-AIF and DS-AIF for all patients in the test set, and is reported as median (IQR), with a Mann–Whitney U test, used to test for significant differences between the AI-AIF and DS-AIF measurements. Bland–Altman analysis was used to assess the bias and limits of agreement between the manual and automated analysis. The linear relationship between stress MBF derived with AI-AIFs and DS-AIFs is visualized with the equation of the line of best with and the associated R 2 value is also reported. This evaluation is performed both on a patient-wise level (MBF averaged over all pixels from each patient) and on an American Heart Association (AHA) 16 segment-wise level. 29 To assess the generalization performance of the AI-AIF model to external data, a further Mann–Whitney U test is performed to test for differences, between internal and external testing data, in the difference between AI-AIF and DS-AIF-derived stress MBF. All statistical analysis was performed in Python using SciPy. 30

In order to also assess the effect of differences in stress MBF between the AI-AIF and DS-AIF methods on the diagnostic accuracy of the quantitative stress MBF values, a further evaluation was conducted with respect to the optimal cut-off threshold for CAD. In particular, a diagnosis would be changed by the use of the AI-AIF if the stress MBF value was lower than the threshold with the AI-AIF and higher with the DS-AIF or vice versa. The diagnosis would be unchanged by the use of the AI-AIF if stress MBF was lower than the threshold with both the AI-AIF and DS-AIF approaches or higher than the threshold with both approaches. In the ideal scenario, the use of the AI-AIF instead of the DS-AIF should leave the diagnosis of all vessels unchanged, and so the percentage of AHA segments for which the diagnoses match is reported. In addition, since the diagnosis for a coronary vessel is made based on the average MBF of the two lowest AHA segments in that coronary territory, 31 the percentage of vessels for which the diagnoses match is also reported. In this study, the quantitative MBF threshold is taken to be 1.35 mL/min/g, as found to be optimal by Hsu et al. 32 using similar methods.

The test data set baseline characteristics are summarized in Table 1 . The AI-AIF model was applied to all patients in the test data set and quantitative perfusion analysis was successfully performed in all 704 AHA segments with both the DS-AIF and AI-AIF methods.

Baseline characteristics

A summary of the baseline characteristics of the test set patient cohort. Data are shown as median (IQR) or n (%).

Figure 2 compares the predicted AI-AIF with the reference standard DS-AIF and the standard high-resolution AIF for three representative patients. A strong agreement is shown in Figure 2A and B with less good agreement seen in Figure 2C . The median NMSE between the DS-AIF and AI-AIF curves was 1.9% (2.5). While the average agreement is good, a similarly bad agreement to Figure 2C (NMSE ≥ 6.5%) is found in 5/44 (11.4%) of test cases. In addition to being similar in terms of absolute error, the quantitative metrics which describes the curves (PV, TTP, and FWHM) are similar between the DS-AIF and AI-AIF, as shown in Figure 3 . There were no significant differences in any of these metrics between the DS-AIF and AI-AIF. The median PV (in normalized signal-intensity units) was 1.48 (0.53) for the DS-AIF and 1.47 (0.44) for the AI-AIF ( P = 0.94), the median TTP was 6.60 s (1.75) for the DS-AIF and 6.60 s (1.76) for the AI-AIF ( P = 0.99), and the median FWHM was 5.34 s (3.09) for the DS-AIF and 5.54 s (2.18) for the AI-AIF ( P = 0.21). This indicates that the AI-AIF model can correct for the signal saturation in a standard single-sequence acquisition and yield curves that closely match those acquired with a dual-sequence.

Example comparison of AI-AIF and DS-AIF curves. A representative set of AIF curves from the test set showing both the DS-AIF and AI-AIF curves in comparison to the saturated standard AIF. ( A ) and ( B ) show strong agreement between the DS-AIF and AI-AIF, as evidenced by the low NMSE. ( C ) shows a less strong agreement in a case where the DS-AIF does not markedly correct for saturation in the AIF.

Quantitative comparison of AI-AIF and DS-AIF curves. Boxplots comparing the distributions of values of the quantitative curve metrics PV ( A ), TTP ( B ), and FWHM ( C ) between the DS-AIF and AI-AIF curves. There is no statistically significant difference between any of the pairs of distributions.

The median MBF was 2.77 mL/min/g (1.08) quantified with the DS-AIF and 2.79 mL/min/g (1.08) quantified with the AI-AIF. There was no statistically significant difference between the approaches ( P = 0.33). There were also no significant differences between the median MBF for the subgroup of patients from centre 1 (2.39 mL/min/g (1.02) vs. 2.49 mL/min/g (1.24), P = 0.31) or the subgroup of patients from centre 2 (3.01 mL/min/g (0.65) vs. 3.08 mL/min/g (1.08), P = 0.49). Three example patients comparing pixelwise MBF maps between the DS-AIF and AI-AIF are shown in Figure 4 . Though subtle differences are apparent between the DS-AIF and AI-AIF-derived maps, the diagnostic information appears to be preserved. There was a significant difference between median MBF at centre 1 of 2.39 mL/min/g (1.02) vs. at centre 2 of 3.01 mL/min/g (0.65) with the DS-AIF, and 2.49 mL/min/g (1.24) vs. 3.08 mL/min/g (1.08) with the AI-AIF ( P < 0.01 and P = 0.02, respectively). However, there was no significant difference ( P = 0.11) in the median difference in MBF between the DS-AIF and AI-AIF at centre 1 [−0.23 mL/min/g (0.48)] and centre 2 [−0.11 mL/min/g (0.62)]. This indicates that the AI-AIF performs similarly well at both centres and that the difference in MBF values between centre 1 and centre 2 was a result of the different patient cohorts or differences in the imaging system related to the field strength or the pulse sequence.

Quantitative MBF maps. A comparison of quantitative MBF maps with both the DS-AIF and AI-AIF for a representative sample of patients, ( A ) and ( B ) from centre 1 and ( C ) from centre 2. There is seen to be a close agreement between the methods, and despite subtle differences, the diagnostic information is visually preserved with the use of the AI-AIF.

There was a strong linear relationship between the MBF values estimated with the DS-AIF and AI-AIF approaches on both per-patient ( y = 0.93 x + 0.28 with the R 2 value of fitting 0.74, Figure 5A ) and per-segment ( y = 0.90 x + 0.37 with the R 2 value of fitting 0.741, Figure 5B ) levels. Additionally, the Bland–Altman analysis (per-patient in Figure 5C and per-segment in Figure 5D ) shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF with a mean bias of −0.11 mL/min/g and limits of agreement that are in line with the inter-study repeatability of stress MBF values. 33 Finally, the effect on the diagnostic accuracy of the use of the AI-AIF in place of the DS-AIF was assessed. The classification of CAD with respect to the optimal MBF threshold agreed for 95.0% (669 out of 704) AHA segments and 89.4% (118 out of 132) coronary vessels.

Comparison of overall MBF values between the DS-AIF and AI-AIF approaches. This shows scatter plots of MBF with the DS-AIF vs. MBF with the AI-AIF, with the line of best fit, its equation and associated R 2 values ( A ) and ( B ), and the Bland–Altman analysis ( C ) and ( D ) on a per-patient (left column) and per-AHA segment basis (right column). The shaded regions represent the 95% confidence intervals for the bias and limits of agreement.

In this work, a deep learning-based correction of the signal saturation in the AIF for quantitative stress perfusion CMR is proposed which has the potential to solve the long-standing issue of the AIF estimation for accurate quantification. To date, there has been limited evidence of quantitative stress perfusion CMR without the use of a modified acquisition scheme, either a dual-bolus contrast injection scheme or a dual-saturation acquisition sequence, but there has been no clinical validation or adoption. 34 The need for modified acquisitions has limited the use of quantitative stress perfusion CMR thus far, but the AI-AIF model has been shown to allow the quantification of perfusion CMR with both a single contrast injection and a standard single-saturation acquisition sequence. Crucially, this is simpler and more available than any other approach for MBF quantification.

Even though the dual-bolus protocol can be used without any additional technology, its clinical adoption has been limited due to the complexity added to the scan and the extra work involved in the two injections. Also, despite being proposed nearly 20 years ago, 35 there has been no widely available implementation of the dual-sequence and no commercial solution, so it is limited to use in a small number of research centres. The code for the AI-AIF model developed in this work is provided as open-source, and thus, is widely available, making it easy to use and not dependent on the availability of technology or the local experience. It is also easily compatible with existing software for quantifying MBF, 6 and so, it can be used in a fully-automated manner. The relative simplicity of the method and availability of the code also makes it straightforward to integrate the AI-AIF with existing perfusion quantification software.

The AI-AIF will not only simplify the scanning workflow, which would make the imaging more likely to be performed and more available, but it is also less prone to human error. The image analysis and post-processing are further simplified, especially compared with the dual-sequence in which an additional imaging slice is acquired that then needs customized processing methods for image registration, segmentation, and proton-density correction. The AI-AIF only requires the standard high-resolution slices, and thus, no customized image processing. As well as being an important future step towards clinical adoption and standardization, since the AI-AIF model is a post-processing step, it will facilitate the retrospective analysis of data acquired without a dual-bolus or dual-sequence. An important application of this would be to retrospectively quantify data from clinical trials, which used visual assessment only, to build further evidence for the use of quantitative MBF.

The results presented in this study evaluate the AI-AIF model compared to the reference standard DS-AIF considering both the direct correspondence of the AIF curves, the quantitative MBF values derived with the AIFs, and the effect of differences in the quantitative MBF values on the patient’s diagnosis. This analysis used both an independent cohort of patients from centre 1 and an external cohort of patients from a second centre to give an idea of the real-world performance with data acquired at a different centre with a different imaging system and a different magnetic field strength. These results are considered to be promising, but it is acknowledged that further clinical validation is required before the potential adoption of the AI-AIF.

As well as closely matching in terms of NMSE (<2%), the AIF curves from the AI-AIF were not significantly different in terms of PV, TTP, or FWHM. The close match in curves resulted in no significant difference in quantitative MBF between the AI-AIF and DS-AIF approaches. The Bland–Altman analysis showed minimal bias and 95% limits of agreement that were in line with the inter-study repeatability of stress MBF values 33 on both per-segment and per-patient levels. Higher MBF was found in the external data from centre 2 but this may be because of differences in the data acquisition or patient cohorts, and even in this subgroup, there is no significant difference between the AI-AIF and DS-AIF. There was no increase in the difference between AI-AIF-based and DS-AIF-based MBF values between the internal and external testing data suggesting that the model can generalize well to new data. It was further shown that, according to a previously published diagnostic cut-off of 1.35 mL/min/g, the diagnosis of the AI-AIF matched the DS-AIF in 669/704 (95%) of segments, indicating that the AI-AIF model can be used without sacrificing diagnostic accuracy compared with the DS-AIF. While these results are generally positive, there were isolated cases with less good agreement for which the resulting analysis may warrant closer inspection if they are to be used for clinical decision-making.

The implementation of the AI-AIF as a retrospective correction step on the AIF curve maintains its flexibility to be used with data from different types of pulse sequences and acquisition schemes. For example, recent work has investigated the acquisition of additional slices to increase spatial coverage 36 but these adaptations would not affect the applicability of the AI-AIF as it can be applied to the AIF curves regardless of how they have been acquired. It is also straightforward to use—so it will not be limited to use in experienced centres with advanced research programs. Since, in addition to the trained model, the scripts for training are also provided, it could also be retrained to adapt to different types of data and improve robustness. This is also relevant for other imaging modalities as it is a general solution, and as discussed by Murthy et al. 37 saturation also occurs with high doses of injected radiotracer activity for perfusion imaging on contemporary 3-dimensional PET systems.

This work focussed on stress MBF only, as opposed to both stress and rest MBF, as it has been shown that the inclusion of the rest images does not improve the diagnostic accuracy. 32 This is in line with recent research 38 and clinical guidelines 39 which suggest the omission of the rest images in order to reduce the scan time. However, the AI-AIF could be easily extended by including the rest images in the training data. Further limitations include that despite testing the model on data from different centres and magnetic field strengths, the data used were from a single scanner manufacturer. Retraining with new data would likely be required to transfer the solution to work with data from other scanner manufacturers. As discussed, a wider variety of patient data sets could also be added to the model training in future work to improve robustness and mitigate potential failures of the model, as shown in Figure 2C . Here, there is a large difference between the AI-AIF and DS-AIF, and this case is shown to represent the worst case in the test set. However, there is seen to be no correction of the AIF with the dual sequence, which is questionable, and shows the limitation of the lack of a true gold standard for validation. The dual-saturation reference standard itself is designed to minimize saturation rather than eliminating it entirely. There may still be residual saturation, for example, due to T2* effects, but it has previously been shown that these effects are small. 40 Furthermore, the quantification step used the simplistic Fermi function deconvolution, and future work could extend this to more complex quantification models, 41 which could even be done using deep learning in combination with the AI-AIF in one model.

This study represents a promising initial proof of concept for applying AI to correct the signal saturation in the AIF for quantitative stress perfusion CMR. However, further work will be required to enable widespread clinical adoption of the approach. In this study, the AI-AIF was compared to the reference standard DS-AIF but the quantitative MBF values obtained with the AI-AIF would need to be validated vs. fractional flow reserve, which is considered the gold standard for identifying ischaemia-related stenosis.

Although the presented test results indicate the performance level in a representative and challenging cohort of patients (widespread cardiovascular risk factors and 48% with a prior history of CAD), the studied cohort is still relatively small. The inclusion of more patients for training will help the capacity of the model to deal with less common cases and more patients for testing will improve confidence in the performance. A more extensive use of data augmentation could also achieve more variability in the training data and a possibility to realize this would be to use a (deep) generative model to generate AIFs for training. Further improvements to the model could be possible by exploring new model architectures or physics-informed learning schemes, but these were not studied in this work, and the U-Net is currently considered one of the methods of choice for signal and image processing.

The availability of the AI-AIF leading to more simplified acquisitions combined with the extensive validation, 31 , 32 , 42 established prognostic significance, 7 , 43 and the advantages of CMR perfusion over single photon emission computed tomography (SPECT) or positron emission tomography (PET) perfusion, including the superior spatial resolution and lack of ionizing radiation, may finally pave the way for more widespread clinical adoption of stress perfusion CMR. As discussed, stress perfusion CMR is gaining clinical relevance due to the growing body of evidence from randomized controlled trials supporting its use 2–4 , 44 but the quantification of MBF will help to reduce the operator-dependence, 5 highlighting the need for accessible quantitative methods. Even more significantly, the quantification of MBF has now been recommended in the American guidelines for the evaluation of patients with chest pain 1 as it is crucial for the evaluation of patients with ischaemia and non-obstructive CAD. 8 , 42 , 45 The proposed method will be important in this context as it makes quantifying stress perfusion CMR as easy as quantifying alternative functional perfusion tests, such as PET. With similar ease-of-use, quantitative perfusion CMR may become the method of choice, due to its higher spatial resolution, as subendocardial ischaemia is a key feature in these patients. 46 , 47

This study presents a step towards the widespread availability of quantitative stress perfusion CMR, an approach for accurate quantification of stress perfusion CMR from single-bolus and single-saturation sequence scans, without any modified acquisition. It uses a deep learning model to correct the signal saturation in the AIF which has been trained using an extensive database of unsaturated AIFs acquired with a dual-saturation acquisition sequence. This is an important step in alleviating the need for labour and time-intensive dual-bolus protocols and for proprietary dual sequence acquisitions, for which there are variable levels of availability. The approach is easy to reproduce or extend as the training code is available, it does not add processing time, and uses a simple model that could be directly incorporated with the scanner. The AI-AIF has the potential to finally advance quantitative stress perfusion CMR from the research domain to integration in routine clinical care.

Contributor Information

Cian M Scannell, School of Biomedical Engineering & Imaging Sciences, King’s College London, 4th Floor Lambeth Wing, St Thomas′ Hospital, London SE1 7EH, UK. Department of Biomedical Engineering, Eindhoven University of Technology, Gemini-Zuid, Groene Loper 5, 5612 Eindhoven, The Netherlands.

Ebraham Alskaf, School of Biomedical Engineering & Imaging Sciences, King’s College London, 4th Floor Lambeth Wing, St Thomas′ Hospital, London SE1 7EH, UK.

Noor Sharrack, Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Clarendon Way, Leeds LS2 9JT, UK.

Reza Razavi, School of Biomedical Engineering & Imaging Sciences, King’s College London, 4th Floor Lambeth Wing, St Thomas′ Hospital, London SE1 7EH, UK.

Sebastien Ourselin, School of Biomedical Engineering & Imaging Sciences, King’s College London, 4th Floor Lambeth Wing, St Thomas′ Hospital, London SE1 7EH, UK.

Alistair A Young, School of Biomedical Engineering & Imaging Sciences, King’s College London, 4th Floor Lambeth Wing, St Thomas′ Hospital, London SE1 7EH, UK.

Sven Plein, School of Biomedical Engineering & Imaging Sciences, King’s College London, 4th Floor Lambeth Wing, St Thomas′ Hospital, London SE1 7EH, UK. Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Clarendon Way, Leeds LS2 9JT, UK.

Amedeo Chiribiri, School of Biomedical Engineering & Imaging Sciences, King’s College London, 4th Floor Lambeth Wing, St Thomas′ Hospital, London SE1 7EH, UK.

This work was supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z] and the Welcome Trust [WT 222678/Z/21/Z]. S.P. is funded by a British Heart Foundation Chair [CH/16/2/32089]. For the purpose of open access, the corresponding author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

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FY 2022 By the Numbers: Extramural Grant Investments in Research

Today we present our annual snapshot of NIH grant funding and success rate data for fiscal year (FY) 2022 enacted appropriations. These data are available in the NIH Data Book , which is also being regularly updated with other FY 2022 grants information. Similar to our FY 2021 and FY 2020 posts, spending related to special appropriations for coronavirus are excluded here, but may be found using RePORTER’s advanced search capabilities .

In FY2022, NIH spent $33.3 billion of its total $45.2 billion appropriation for competing and noncompeting grant awards. This is a 3.1% increase (or $1.02 billion) in spending over the previous year. Monies for grants and Other Transaction awards are included while research and development contracts are excluded.

NIH supported 1,576 additional new and renewed extramural grants in FY 2022, for a total of 58,368 competing and non-competing awards (2.8% more than FY 2021). NIH issued grants to 2,707 academic universities, hospitals, small businesses, and other organizations throughout the U.S. and internationally.

Table 1 – All Extramural Research (competing and non-competing, excluding contracts)

The success rate for new research project grants (RPGs) increased 1.6 percentage points from 19.1% in FY 2021 to 20.7% in FY 2022. This is because we received 4,301 fewer RPG competing applications in FY 2022 compared to the previous year (54,571 compared to 58,872), while making 82 more awards (11,311 compared to 11,229). The average nominal cost per RPG rose by 1.9% in 2022 to $592,617 from $581,293 in FY 2021.

Table 2 – Research Project Grants (RPG)

* Success rates are calculated by dividing the number of awards made in a FY by the number of applications received. Applications having one or more amendments in the same fiscal year are only counted once.

Most RPGs are R01-equivalent grants , and they showed similar trends. We spent $19.1 billion on average on R01-equivalent grants in FY 2022 compared to $18.1 billion spent in FY 2021, a 5.4% increase. Like RPGs, the R01-equivalent grant success rate also increased (1.5 percentage points), going from 20.1% in FY 2021 to 21.6% in FY 2022. We spent 2.4% more in average nominal costs on R01-equivalents in FY 2022 ($585,307) compared to $571,561 spent in FY 2021.

Table 3 – R01-equivalent Grants**

**R01-equivalent grants are defined as activity codes DP1, DP2, DP5, R01, R37, R56, RF1, RL1, U01 and R35 from select National Institute of General Medical Sciences and National Human Genome Research Institute program announcements. Not all these activities may be in use by NIH every year.

Please note that NIH does not report the number of applications received in specific research areas, and thus does not report success rates for those areas either.

I would like to thank my colleagues within the NIH Office of Extramural Research’s Division of Statistical Analysis and Reporting for their work on this analysis.

Correction: Previously, we accidently misreported the FY 2021 success rate for R01-equivalent grants. We have corrected the data table to reflect this change, which now makes the percent change calculation correct.

RELATED NEWS

Please check “2022 % Change from 2021” for “Success rates for R01-equivalent applications:”. How did you calculate a 7.5% increase?

Thank you for bringing this to our attention. We accidently misreported the FY 2021 success rate for R01-equivalent grants. We have corrected the data table to reflect this change, which now makes the percent change calculation correct.

Thank you so much for this reporting. I would be interested to learn about data on 1) the average cut in budget made by the NIH before award is made (% of budget) and 2) the incidence in which the duration of the project has been reduced. This latter issue occurs both to align the year-end reporting with NIH workload (resulting in a short first year), but also occurs with an entire year is taken off the project to save money (e.g., a five-year grant is cut to 4 years).

Thank you so much. Could you also report on the numbers and percentages for women and underrepresented researchers?

These success rates seem higher than posted paylines for many institutes and how many colleagues are getting funded. Is this success % of all grants submitted, or those discussed?

You show that there are 7,816 new or renewal R01-equivalent grants. What is the total number of active R01-equivalent grants?

What are the non-R01-equivalent research project grants?

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https://www.nist.gov/identity-access-management/collaborative-research-digital-identity-public-benefits-delivery

Identity & access management

Collaborative research on digital identity in public benefits delivery.

Empowering state agency leadership to weigh both access and security In delivering vital public benefits.

About the project

NIST, along with the  Digital Benefits Network (DBN) at the Beeck Center for Social Impact + Innovation at Georgetown University, and the  Center for Democracy and Technology (CDT) announced the launch of a two-year-long collaborative research and development project. This project works to adapt NIST’s  digital identity guidelines to better support the implementation of public benefits policy and delivery while balancing security, privacy, equity, and usability. This work is the result of a Cooperative Research and Development Agreement (CRADA).

The project will rely on the tried-and-true process of robust community engagement to develop voluntary resources with the aim of garnering input from a variety of voices (including federal partners, state benefit program administrators, state IT and cybersecurity leaders, digital identity experts, technologists, advocates, and those with direct experience navigating the U.S. public benefit landscape). At the conclusion of the project, the collaboration will yield a voluntary community profile of NIST’s Digital Identity Guidelines ( Special Publication 800-63 ) to support and empower practitioners and public sector leaders in evaluating the necessity and degree of authentication and identity-proofing practices in benefits delivery. 

NIST, DBN, and CDT recognize that agencies face significant challenges in protecting beneficiary information and ensuring the integrity of their programs (while also noting the urgent need for clear, comprehensive resources for state benefits-administering agencies as they adopt authentication and identity-proofing technologies). Appropriately balancing access and security-- while taking into account nuanced program circumstances and populations-- is vital to meaningfully improving public benefits and delivery.

Read the News Release: https://www.nist.gov/news-events/news/2024/06/nist-launches-collaborative-research-effort-digital-identity-support-secure

Email us about this project: benIDprofile [at] georgetown.edu ( benIDprofile[at]georgetown[dot]edu )   Email us about SP 800-63: dig-comments [at] nist.gov (dig-comments[at]nist[dot]gov)

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Commonwealth Cyber Initiative funds 11 inclusive cybersecurity projects

Researchers from Virginia Tech, University of Virginia, William & Mary, George Mason University, and Old Dominion University are addressing inclusion and accessibility issues in cybersecurity.

Michele McDonald

10 Jun 2024

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Virginia researchers are working to ensure people feels safer and that their privacy is more protected on computer networks and other devices through a new inclusive cybersecurity program funded by the Commonwealth Cyber Initiative (CCI). 

CCI awarded 11 projects as part of its 2024  Addressing Inclusion and Accessibility in Cybersecurity Program .

“Cybersecurity helps protect our data and the infrastructure we use every day,” said Luiz DaSilva, CCI executive director. “Our first inclusive cybersecurity program is addressing some of the challenges in making sure that security reaches as many people as possible. CCI researchers will be addressing authentication, social bias, vulnerability measures for older adults, and more. We are excited about the new opportunities and impact this research could have on our communities.”

Researchers are from  Virginia Tech ,  George Mason University , the University of Virginia ,  William & Mary , and Old Dominion University .

The CCI hub in Arlington funded six projects with additional funding for projects coming from CCI’s Coastal Virginia Node for one project, Northern Virginia Node for two projects, and Southwest Virginia Node for two projects.

The strength of the proposals prompted CCI node directors to fund additional projects. 

CCI Northern Virginia Node Director Liza Wilson Durant said, “As CCI breaks new ground with new cybersecurity solutions and technologies, we want to invest in solutions that meet a diversity of user needs. This requires intentional investment and research that leverages a diversity of disciplines, not only computing and engineering but also the social sciences for example. 

“We are excited by the potential impact these transdisciplinary teams will have on cybersecurity challenges across diverse populations,” she said.

CCI Southwest Virginia Node Director Gretchen Matthews said, “The CCI Southwest Virginia Node wanted to invest in this type of research to ensure that cybersecurity efforts reach some of the most vulnerable communities. Our hope is that this program will continue and have long lasting impacts.”

The CCI Inclusion and Diversity Committee helped develop the program. 

“CCI is expanding our understanding of cybersecurity and identifying gaps, specifically in accessibility and inclusion," said Nathan Carter , committee chair. “The inclusive cybersecurity program not only highlights the depth of expertise in Virginia, but also the keen interest from researchers to address this crucial need. The CCI Inclusion and Diversity Committee is excited to see the impact this program will have on the commonwealth and the nation.”

About the funded projects

Marcos  Zampieri of the George Mason University School of Computing Department of Information Sciences and Technology will study content moderation to better understand human and machine perception of what others might find offensive by exploring how one group thinks another group will perceive potentially offensive content. The goal is to develop more inclusive, transparent, and trustworthy content moderation processes. 

Vikas Ashok of the Old Dominion University (ODU) Department of Computer Science and Faryaneh Poursardar of ODU’s Virginia Modeling and Simulation Center , will plug a gap in dark-pattern research by developing counter-measures to mitigate deceptive patterns on websites that are specific to blind and low vision users who use assistive technologies.

Lannan Lisa Luo and Qiang Zeng of George Mason University’s Computer Science Department will address accessibility gaps in authentication technologies, ensuring that individuals with disabilities can seamlessly and securely interact with everyday objects in their environments, facilitating their independence and fostering a sense of inclusiveness in their interactions with the world.

Emanuela Marasco of George Mason University's Center for Secure Systems will develop a contactless biometric mobile security application to correct the vulnerabilities of deep artificial intelligence (AI) and optical sensors and allow marginalized people equal access to data security regardless of gender and skin tone.

Hemant Purohit of the George Mason University School of Computing , Fengxiu Zhang of Mason’s Schar School of Policy and Government , Jin Hee Cho and Chang-Tien Lu of Virginia Tech’s Department of Computer Science Department , and Michin Hong of Indiana University’s School of Social Work will develop a method to use the Social Cyber Vulnerability Index to detect fraud in online social spaces while factoring in demographic behavioral vulnerabilities that introduce representation bias due to disparities in detection-model training. 

Abhijit Sarkar of the Virginia Tech Transportation Institute and Lynn Abbott of Virginia Tech's Bradley Department of Electrical and Computer Engineering aim to build an inclusive cyber system using biometric authentication derived from the cardiovascular system, which will ensure that security systems are accessible and fair to everyone, irrespective of skin tone, gender, age, or physical ability. Cardiac biometrics depend on internal characteristics rather than external appearance.

Bimal Viswanath of Virginia Tech’s Department of Computer Science , Yixin Sun of the University of Virginia’s (UVA) Department of Computer Science , and Lanfei Shi of UVA’s McIntire School of Commerce will create tools to counteract threats from AI-driven malicious entities on social media platforms. Raising awareness of these threats will facilitate the development of effective strategies to safeguard marginalized and vulnerable communities from exploitation.

Ziwei Zhu of George Mason University’s Department of Computer Science and   Jin R. Lee of Mason’s Department of Criminology, Law and Society aim to significantly improve the inclusion and accessibility of Large Language Model applications in cybersecurity by addressing algorithmic bias, focusing on reducing unfair treatment against vulnerable populations identified by gender, race, religion, sexual orientation, and disability. 

Mary L. Still of Old Dominion University’s Department of Psychology and Jeremiah D. Still of ODU’s School of  Cybersecurity will revise complex cyber hygiene materials to present information in plain language to promote equitable digital access. They’ll develop inclusive design guidelines for those with comprehension challenges associated with digital literacy and reading comprehension, benefiting vulnerable groups as well as the general population. 

Yanfu Zhang and  Qun Li of William & Mary’s Department of Computer Science will develop password technology for those with mobility impairments, harnessing Brain-Computer Interface systems to create a machine learning system using portable electroencephalogram headsets to extract passwords from users’ brain signals. 

Tabitha L. James and Viswanath Venkatesh of Virginia Tech’s Pamplin College of Business will study the impact of judgmental or “judgy AI” on vulnerable populations such as minorities and the underprivileged. Understanding of the effects of judgy AI systems can have design, regulatory, and legal ramifications for the appropriate use of such tools in business.

• Commonwealth Cyber Initiative
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• Inclusion and Diversity
• Pamplin College of Business
• Research Frontiers
• Sanghani Center for Artificial Intelligence and Data Analytics
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3D printing team adds value to any research project

BY MICHAEL ELLIS LANGLEY

THURSDAY, JUNE 13, 2024

Brandon Langdon and his team know they can add something to your project — something you may not be able to get through traditional methods of creating parts.

Brandon is one of Sandia California’s electromechanical technologists, which is to say he knows how to 3D print very complex items — items that can be really small, created for a specific use and even made out of metals like titanium.

“We’re doing things that are research-oriented, providing parts in materials that need to be tested, as well as internal parts for complex systems,” Brandon said. “We are characterizing materials and evolving standards for international standards communities like American Society for Testing and Materials International and SAE International.”

A 3D-metal printer works by adding layers of metal powder in a pattern then using one or two lasers to shape the emerging part. Brandon programs laser parameters, designs parts with varying geometry and then figures out how to bring it all together in the machine with the right powder composition. It’s work that requires extensive collaboration with researchers who want to create and test a shape or part.

“There is currently a push from NNSA and Sandia management to use the additive processes in future component designs,” said nanomaterials scientist Josh Sugar, who worked with Brandon and his team on a project. “It is essential that the research and development community partner with the additive materials lab to produce materials and components that can be studied and tested.”

Brandon said the lab has a deep set of applications they can manufacture.

“We can work on materials characterization for chemists,” he said. “There are some materials that we can print that are bio-safe for implanting in bodies, and that’s really interesting. We make connector brackets or things that might be challenging to manufacture. We did a part for researchers who needed a certain texturing on a small area, but it was just too small to machine. We 3D printed the whole part, adding threads and texture where needed.”

Brandon and the additive manufacturing team have helped develop pieces to test hydrogen penetration and degradation of various materials and a host of other work that enables other research teams to move forward quickly, without waiting for a commercial part or having to buy many types of materials. The lab can even produce a thin layer, or coat, of a material for testing.

“One advantage of doing spray coating or electroplating, is that if you have an expensive material or a rare material, you don’t have to get a billet of it and machine it out,” Brandon said. “We can just do a surface coating a few microns thick and then have the rest of it just be some inexpensive steel or titanium that you know is much easier to work with.”

With a new piece of equipment in Sandia California’s additive manufacturing lab — a selective laser melting printer — the team can create intricate designs in titanium. The additive manufacturing process allows the team to produce objects that have open internal channels or optimize an existing design.

“If there was something we’re trying to improve on, like maybe it was just too heavy, I can help take that existing design and very quickly make it lighter,” Brandon said.

Working closely with Sandian colleagues gives the additive manufacturing team better insight into how to solve their modeling and manufacturing issues, resulting in products that are fit specifically for their needs. Josh said part of that collaboration involves learning what is possible.

“We need to understand additive processes at a fundamental level so that we can design and build parts with reliable and predictable properties over their lifetime,” he said. “We also need to be able to write specifications so that our partner production sites are successful at manufacturing parts that meet these requirements.”

Brandon said that they are able to work with groups that aren’t sure exactly what they need.

“If people want to play around with some ideas, we can print 12 small things on the plate at the same time rather than having to machine each one, one at a time,” he said. “We might be able to offer you a quicker and faster way to get closer to a prototype.”

“If everything’s letting you down on the traditional ways of doing things, then additive can pick up a lot sometimes because maybe there’s a reason it’s not working for you in the design space,” Brandon added. “You’re maybe outside of the envelope for traditional manufacturing or maybe your constraints are in such a way that you need to simplify the part.”

Brandon said his team wants to help anyone they can with their research.

“A successful partnership between the additive manufacturing lab and the R&D community enables success in these endeavors,” Josh said.

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UK project on bacteria-focused research selected for Hypothesis Fund seed grant

LEXINGTON, Ky. (June 14, 2024) — A researcher at the University of Kentucky has been selected to receive a seed grant from the Hypothesis Fund for the “boldness of her science and potential long-term impact of her work.”

The Hypothesis Fund supports early-stage, innovative research focused on addressing systemic risks to human and planetary health. They offer seed grants for novel, very early-stage research projects, with the goal of sparking new basic science discoveries.

Natalia Korotkova, Ph.D., is an assistant professor in the Department of Microbiology, Immunology and Molecular Genetics in the College of Medicine . As a microbiologist and biochemist, her work focuses on understanding cell biology of bacteria.

Her project titled “Functional significance of extracytoplasmic intrinsically disordered regions in streptococci” was selected for a seed grant by a Hypothesis Fund Scout — outstanding scientists who identify other scientists to fund.

“I am grateful to the Hypothesis Fund Scout, Dr. Mougous, who saw the potential of my work and pushed for this seed grant,” said Korotkova. “I’m looking forward to what we’ll discover through this project and the impact it could have on Kentucky and our country.”

Korotkova will study the roles and activities of specific parts of membrane proteins in streptococcal bacteria to determine how those contribute to the bacteria’s function, including interacting with a host or adapting to environments.

Korotkova was nominated by Joseph Mougous, Ph.D., a professor of microbiology at the University of Washington.

“Dr. Korotkova is seamlessly combining bacterial genetics and physiology with in-depth biochemistry to understand the regulatory mechanisms of intrinsically disordered regions (IDR) in bacterial proteins — all while keeping an eye on the human pathogenic side of the organisms,” said Mougous. “Her project could reveal bacterial IDRs as a fruitful and important area of investigation, with especially broad ramifications on the field of microbiology.”

As the state’s flagship, land-grant institution, the University of Kentucky exists to advance the Commonwealth. We do that by preparing the next generation of leaders — placing students at the heart of everything we do — and transforming the lives of Kentuckians through education, research and creative work, service and health care. We pride ourselves on being a catalyst for breakthroughs and a force for healing, a place where ingenuity unfolds. It's all made possible by our people — visionaries, disruptors and pioneers — who make up 200 academic programs, a $476.5 million research and development enterprise and a world-class medical center, all on one campus.   

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Eight innovative projects receive 2024 internal awards

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Eight innovative projects have been named recipients of Auburn University’s internal research and creative works funding awards programs for 2024.

The Research Support Program (RSP) and the Creative Work and Social Impact Scholarship Funding Program (CWSIS) were established by the Office of the Senior Vice President for Research and Economic Development. Both programs provide a competitive internal funding source to support faculty members’ research and creative scholarship as they refine their projects before competing for larger, external awards.

“The CWSIS and RSP provide applicants across all disciplines with the opportunity to participate in a competitive funding program,” said Christine Cline, associate director of Proposal Services and Faculty Support, the unit administering the programs. “The programs support a wide variety of research initiatives from creative work through STEM-focused projects. We appreciate the opportunity to engage with the participants from the initiation of the application cycle through the successful completion of their projects.”

The RSP is intended to foster the development and growth of innovative and transformational research activities. It builds on faculty expertise, stimulates interdisciplinary collaborations and strengthens seed research activities. It is a strategically focused Auburn investment that promotes promising and impactful new lines of research as well as the growth of collaborative and interdisciplinary teams to build the foundations of science, to overcome scientific and societal challenges and to promote and enhance the quality of life and well-being of individuals, groups and communities.

The CWSIS funding program fosters innovation and discovery and builds faculty reputation and competitiveness. Disciplines associated with CWSIS include design and the arts, humanities and applicable areas within business, education, social sciences and health and well-being.

This year’s recipients are:

Research Support Program

James Gillespie , College of Veterinary Medicine, “Development of Bacteriophage Nano/Microparticles for Nasal Delivery of Species-specific Immunocontraceptives” Co-Investigator: Constantinos Kyriakis, College of Veterinary Medicine

Suhasini Gururaja , Samuel Ginn College of Engineering, “Integrated Manufacturing for ‘Tuned’ Microstructures for Targeted Enhanced Lightweight Structural Performance and Autonomous Damage Sensing (IMADS)” Co-Investigator: Virginia Davis, Samuel Ginn College of Engineering

Junshan Lin , College of Sciences & Mathematics, “Computation-assisted Optical Imaging towards Sub-Nanometer Super-resolution” Co-Investigator: Siyuan Dai, Samuel Ginn College of Engineering

Binita Mahato , College of Liberal Arts, “Urban Resilience and Social Vulnerability: The Past, Present, and Future of Climate Change Impacts in Mobile, Alabama” Co-Investigators: Chandana Mitra, College of Science and Mathematics; Dr. Jake Nelson, College of Science and Mathematics; Rebecca Retzlaff, College of Liberal Arts

Yaoqi Zhang , College of Forestry, Wildlife and Environment, “Tree Shade on Summertime Electricity Consumption” Co-Investigator: Wenying Li, College of Agriculture

Creative Work and Social Impact Scholarship Funding Program

Georges Fares , College of Human Sciences, “Bridging Eras: Merging Technology and Tradition in the Study of Bernini’s Works for the Purpose of Cultural Engagement” Co-Investigator: Anna Ruth Gatlin, College of Human Sciences

Allie McCreary , College of Forestry, Wildlife and Environment, “Climate Change Resiliency Along the Gulf Coast: How Tourism Providers Perceive Impacts & Adaptation Strategies”

Xavier Vendrell , College of Architecture, Design & Construction, “Site, Construction, Users, My Favorite Movies, and Other Circumstances:  Xavier Vendrell, Architect”

More information about these and other funding support programs supported by the Auburn Office of the Senior Vice President for Research and Economic Development can be found by  clicking here.

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Renovations approved to improve safety and efficiency of classrooms, research laboratories

The Indiana University Board of Trustees approved one architectural design and several facilities projects during its June meeting in Bloomington, including:

• The architectural designs for the IU Indianapolis Science Laboratory expansion and renovation project.
• Renovations to the IU School of Medicine’s VanNuys Medical Science Building Laboratory on the Indianapolis campus.
• Renovation and rehabilitation to chemistry teaching labs on the IU Bloomington campus.
• Replacement, upgrades and expansion of cooling capacity and distribution on the IU Bloomington campus.
• The 2025 Repair and Rehabilitation Plan for all IU campuses.
• 2025 Regional Campus Deferred Maintenance projects at five regional campuses.

IU Indianapolis Science Laboratory Building

The architectural designs for the IU Indianapolis Science Laboratory Building will introduce a cutting-edge, multidisciplinary research facility spanning 52,000 square feet, strategically positioned at the intersection of North Blackford and West New York streets in the IU Indianapolis Science and Technology Corridor.

The architecture of the new building is designed to complement the existing Science and Engineering Laboratory Building at IU Indianapolis and address the pressing need for enhanced research infrastructure to accommodate the campus’s growth.

The trustees approved this $60 million expansion and renovation project at their June 15, 2023, meeting and the project was also approved for state funding during the 2023 Indiana General Assembly as part of IU’s 2023-25 Capital Appropriation Request.

IU School of Medicine VanNuys Medical Science Building Laboratory

This project will renovate academic spaces across areas of the first, second and third floors of the North Wing of the IU School of Medicine’s VanNuys Medical Science Building at IU Indianapolis to convert existing office and classroom space into new research laboratories.

The anticipated opening of IU’s new Medical Education and Research Building provides opportunities to renovate existing office and classroom space in the VanNuys building to meet IU’s research laboratory goals. These new units in the VanNuys building will allow the university to explore how common research equipment can be efficiently leveraged by placing researchers with similar interests or approaches in close proximity.

In addition to academic space, renovations will include interior structural, mechanical and infrastructure updates and installation of new laboratory utilities, HVAC, plumbing, fire protection, power, data and finishes as required for new laboratory layouts. In all, the project will cover a total area of 28,099 square feet.

Chemistry teaching labs at IU Bloomington

This project will renovate eight teaching laboratories and related support areas in the Chemistry Building on the IU Bloomington campus, as well as replace four air handling units that serve these laboratories and related areas in the building.

Upgrades to more than 31,000 square feet — including more than 19,000 square feet of academic space — will be made in support of IU Bloomington Provost Rahul Shrivastav’s Project Inspire, a multiyear effort to systematically improve and update instructional spaces on the Bloomington campus.

The chemistry lab project will reconfigure and modernize 1980s-era teaching laboratories to improve safety, sightlines and accessibility, enabling flexible and collaborative instruction. Nearly 100 new fume hoods — which make use of high-efficiency, low-flow technology for significant energy savings, as well as improved laboratory safety — will be installed as well. The project will replace the building’s central heating, ventilation and air conditioning systems.

Work will be completed in two phases, allowing for uninterrupted instructional delivery during the construction process.

Cooling capacity and distribution at IU Bloomington

Phase one of a two-phase project will replace two aging chillers at the Main Chilled Water Plant and add one additional chiller in support of growing energy management and utility distribution needs on the Bloomington campus.

Since 2015, more than 2.1 million gross square feet of space has been added to the central chilled water system. This increase, especially during periods of extreme or prolonged periods of heat, has significantly increased cooling demand. New higher-capacity replacement chillers will operate more efficiently while largely making use of existing plant infrastructure.

In addition to the new chillers, this project will connect the standalone Union Street plant to the Forest Quad plant, adding Union Street to the central system and expanding reliability and diversity of cooling capacity campus-wide. The new chiller equipment as well as the physical connection of the Union Street plant will add a net increase of more than 3,200 tons of additional capacity — a 16% increase — to IU Bloomington’s central chilled water loop.

2025 Repair and Rehabilitation Plan

Funded by state appropriation and student fees, the 2025 Repair and Rehabilitation Plan includes repairs or replacement of roofing; windows; elevators; electrical, fire protection, mechanical and plumbing systems; steam, utilities, and electricity distribution systems; and classroom and site improvements at IU Bloomington, IU Indianapolis and all regional campuses.

2025 Regional Campus Deferred Maintenance

The Regional Campus Deferred Maintenance projects impact 14 buildings on five campuses and will provide safe, effective and efficient learning and work environments for students, faculty and staff through repairs and renovations of facilities and infrastructure.

Renovations will include replacing or updating building heating systems, mechanical systems and controls, and will continue to address work begun in Phases I-V of the Regional Campus Deferred Maintenance requests. This project was previously funded by the Indiana General Assembly in the 2023-2025 State Budget and was part of Indiana University’s 2023-25 Capital Appropriation Request.

Marah Yankey

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