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1. Correcting motion induced fluorescence artifacts in two-channel neural imaging

   https://doi.org/10.1371/journal.pcbi.1010421  

   Creamer, Matthew S. ; Chen, Kevin S. ; Leifer, Andrew M. ; Pillow, Jonathan W. ( September 2022 , PLOS Computational Biology)

   Louis, Matthieu (Ed.)

   Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal’s movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of interest. One approach to mitigating these artifacts is to image two channels simultaneously: one that captures an activity-dependent fluorophore, such as GCaMP, and another that captures an activity-independent fluorophore such as RFP. Because the activity-independent channel contains the same motion artifacts as the activity-dependent channel, but no neural signals, the two together can be used to identify and remove the artifacts. However, existing approaches for this correction, such as taking the ratio of the two channels, do not account for channel-independent noise in the measured fluorescence. Here, we present Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove artifacts by specifying a generative model of the two channel fluorescence that incorporates motion artifact, neural activity, and noise. We use Bayesian inference to infer latent neural activity under this model, thus reducing the motion artifact present in the measured fluorescence traces. We further present a novel method for evaluating ground-truth performance of motion correction algorithms by comparing the decodability of behavior from two types of neural recordings; a recording that had both an activity-dependent fluorophore and an activity-independent fluorophore (GCaMP and RFP) and a recording where both fluorophores were activity-independent (GFP and RFP). A successful motion correction method should decode behavior from the first type of recording, but not the second. We use this metric to systematically compare five models for removing motion artifacts from fluorescent time traces. We decode locomotion from a GCaMP expressing animal 20x more accurately on average than from control when using TMAC inferred activity and outperforms all other methods of motion correction tested, the best of which were ~8x more accurate than control. 

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2. The Benefit of Distraction: Denoising Camera-Based Physiological Measurements Using Inverse Attention

   Nowara, Ewa M. ( October 2021 , Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021)

   null (Ed.)

   Attention networks perform well on diverse computer vision tasks. The core idea is that the signal of interest is stronger in some pixels ("foreground"), and by selectively focusing computation on these pixels, networks can extract subtle information buried in noise and other sources of corruption. Our paper is based on one key observation: in many real-world applications, many sources of corruption, such as illumination and motion, are often shared between the "foreground" and the "background" pixels. Can we utilize this to our advantage? We propose the utility of inverse attention networks, which focus on extracting information about these shared sources of corruption. We show that this helps to effectively suppress shared covariates and amplify signal information, resulting in improved performance. We illustrate this on the task of camera-based physiological measurement where the signal of interest is weak and global illumination variations and motion act as significant shared sources of corruption. We perform experiments on three datasets and show that our approach of inverse attention produces state-of-the-art results, increasing the signal-to-noise ratio by up to 5.8 dB, reducing heart rate and breathing rate estimation errors by as much as 30 %, recovering subtle waveform dynamics, and generalizing from RGB to NIR videos without retraining. 

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3. PulseCam: a camera-based, motion-robust and highly sensitive blood perfusion imaging modality

   https://doi.org/10.1038/s41598-020-61576-0  

   Kumar, Mayank ; Suliburk, James W. ; Veeraraghavan, Ashok ; Sabharwal, Ashutosh ( March 2020 , Scientific Reports)

   Blood carries oxygen and nutrients to the trillions of cells in our body to sustain vital life processes. Lack of blood perfusion can cause irreversible cell damage. Therefore, blood perfusion measurement has widespread clinical applications. In this paper, we develop PulseCam — a new camera-based, motion-robust, and highly sensitive blood perfusion imaging modality with 1 mm spatial resolution and 1 frame-per-second temporal resolution. Existing camera-only blood perfusion imaging modality suffers from two core challenges: (i) motion artifact, and (ii) small signal recovery in the presence of large surface reflection and measurement noise. PulseCam addresses these challenges by robustly combining the video recording from the camera with a pulse waveform measured using a conventional pulse oximeter to obtain reliable blood perfusion maps in the presence of motion artifacts and outliers in the video recordings. For video stabilization, we adopt a novel brightness-invariant optical flow algorithm that helps us reduce error in blood perfusion estimate below 10% in different motion scenarios compared to 20–30% error when using current approaches. PulseCam can detect subtle changes in blood perfusion below the skin with at least two times better sensitivity, three times better response time, and is significantly cheaper compared to infrared thermography. PulseCam can also detect venous or partial blood flow occlusion that is difficult to identify using existing modalities such as the perfusion index measured using a pulse oximeter. With the help of a pilot clinical study, we also demonstrate that PulseCam is robust and reliable in an operationally challenging surgery room setting. We anticipate that PulseCam will be used both at the bedside as well as a point-of-care blood perfusion imaging device to visualize and analyze blood perfusion in an easy-to-use and cost-effective manner.

    

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4. A Computational View of the Emotional Regulation of Disgust using Multimodal Sensors

   https://doi.org/10.1109/FG47880.2020.00145  

   Kota, Vidhathri ; Gali, Geeta Madhav ; Nwogu, Ifeoma ( November 2020 , 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020))

   null (Ed.)

   Emotion regulation can be characterized by different activities that attempt to alter an emotional response, whether behavioral, physiological or neurological. The two most widely adopted strategies, cognitive reappraisal and expressive suppression are explored in this study, specifically in the context of disgust. Study participants (N = 21) experienced disgust via video exposure, and were instructed to either regulate their emotions or express them freely. If regulating, they were required to either cognitively reappraise or suppress their emotional experiences while viewing the videos. Video recordings of the participants' faces were taken during the experiment and electrocardiogram (ECG), electromyography (EMG), and galvanic skin response (GSR) readings were also collected for further analysis. We compared the participants behavioral (facial musculature movements) and physiological (GSR and heart rate) responses as they aimed to alter their emotional responses and computationally determined that when responding to disgust stimuli, the signals recorded during suppression and free expression were very similar, whereas those recorded during cognitive reappraisal were significantly different. Thus, in the context of this study, from a signal analysis perspective, we conclude that emotion regulation via cognitive reappraisal significantly alters participants' physiological responses to disgust, unlike regulation via suppression. 

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5. A methodological approach for researching online teacher professional development

   Johnson, M. M. ( April 2021 , Middle Atlantic ASEE Section Spring 2021 Conference)

   null (Ed.)

   COVID-19 has altered the landscape of teaching and learning. For those in in-service teacher education, workshops have been suspended causing programs to adapt their professional development to a virtual space to avoid indefinite postponement or cancellation. This paradigm shift in the way we conduct learning experiences creates several logistical and pedagogical challenges but also presents an important opportunity to conduct research about how learning happens in these new environments. This paper describes the approach we took to conduct research in a series of virtual workshops aimed at teaching rural elementary teachers about engineering practices and how to teach a unit from an engineering curriculum. Our work explores how engineering concepts and practices are socially constructed through interactions with teachers, students, and artifacts. This approach, called interactional ethnography has been used by the authors and others to learn about engineering teaching and learning in precollege classrooms. The approach relies on collecting data during instruction, such as video and audio recordings, interviews, and artifacts such as journal entries and photos of physical designs. Findings are triangulated by analyzing these data sources. This methodology was going to be applied in an in-person engineering education workshop for rural elementary teachers, however the pandemic forced us to conduct the workshops remotely. Teachers, working in pairs, were sent workshop supplies, and worked together during the training series that took place over Zoom over four days for four hours each session. The paper describes how we collected video and audio of teachers and the facilitators both in whole group and in breakout rooms. Class materials and submissions of photos and evaluations were managed using Google Classroom. Teachers took photos of their work and scanned written materials and submitted them all by email. Slide decks were shared by the users and their group responses were collected in real time. Workshop evaluations were collected after each meeting using Google Forms. Evaluation data suggest that the teachers were engaged by the experience, learned significantly about engineering concepts and the knowledge-producing practices of engineers, and feel confident about applying engineering activities in their classrooms. This methodology should be of interest to the membership for three distinct reasons. First, remote instruction is a reality in the near-term but will likely persist in some form. Although many of us prefer to teach in person, remote learning allows us to reach many more participants, including those living in remote and rural areas who cannot easily attend in-person sessions with engineering educators, so it benefits the field to learn how to teach effectively in this way. Second, it describes an emerging approach to engineering education research. Interactional ethnography has been applied in precollege classrooms, but this paper demonstrates how it can also be used in teacher professional development contexts. Third, based on our application of interactional ethnography to an education setting, readers will learn specifically about how to use online collaborative software and how to collect and organize data sources for research purposes. 

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