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1. Exceptional stratospheric contribution to human fingerprints on atmospheric temperature

   https://doi.org/10.1073/pnas.2300758120  

   Santer, Benjamin D. ; Po-Chedley, Stephen ; Zhao, Lilong ; Zou, Cheng-Zhi ; Fu, Qiang ; Solomon, Susan ; Thompson, David W. ; Mears, Carl ; Taylor, Karl E. ( May 2023 , Proceedings of the National Academy of Sciences)

   In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO 2 should warm Earth’s troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth’s surface (S 25 − 50 ). To date, however, S 25 − 50 temperatures have not been used in pattern-based attribution studies of anthropogenic climate change. Here, we perform such a “fingerprint” study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S 25 − 50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S 25 − 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S 25 − 50 signal and noise patterns are accompanied by large cooling of S 25 − 50 (1 to 2 ° C over 1986 to 2022) and low S 25 − 50 noise levels. Our results explain why extending “vertical fingerprinting” to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth’s atmosphere. 

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2. Personalized Pain Detection in Facial Video with Uncertainty Estimation

   Xu, Xiaojing ; de Sa, V.R. ( January 2021 , 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC))

   null (Ed.)

   Pain is a personal, subjective experience, and the current gold standard to evaluate pain is the Visual Analog Scale (VAS), which is self-reported at the video level. One problem with the current automated pain detection systems is that the learned model doesn’t generalize well to unseen subjects. In this work, we propose to improve pain detection in facial videos using individual models and uncertainty estimation. For a new test video, we jointly consider which individual models generalize well generally, and which individual models are more similar/accurate to this test video, in order to choose the optimal combination of individual models and get the best performance on new test videos. We show on the UNBCMcMaster Shoulder Pain Dataset that our method significantly improves the previous state-of-the-art performance. 

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3. Sensory prosthetics - clinical and scientific utility of a vestibular implant

   Haburcakova, C. ; Merfeld, D. ; Gong, W. ; Guinand, N. ; Perez, Fornos A. ; Thompson, L.A. ; Guyot, J.P. ; Lewis, R.F. ( April 2017 , Neurology)

   Objective: To determine if a vestibular prosthesis could improve function in subjects with severe vestibular damage and could be used it as a scientific tool to investigate central vestibular processing. Background: Damage to the vestibular labyrinth is common and usually permanent. We therefore developed and tested a vestibular implant (VI) that is designed to mimic the information normally provided by the vestibular labyrinth to determine if we can reduce vestibular-mediated deficits and study temporal integration of sensory cues in the brain. Design/Methods: Monkeys had electrodes implanted in the semicircular canals of one ear and then severe bilateral vestibular damage was induced with aminoglycosides. Eye movements, perception, and balance were tested before and after vestibular damage and with the VI activated, which supplied head motion information to the brain via electrical stimulation delivered by the implanted electrodes. Humans also had electrode implantation (done in conjunction with a cochlear implant, CI) and they were tested on a temporal binding psychophysical task Results: Stimulation provided by VI in vestibulopathic monkeys improved their balance, perception of spatial orientation, and eye movement responses. Timing experiments in humans using CI and VI stimuli showed that unlike past experiments that used motion to generate the vestibular signal, CI and VI signals were received by the cerebral cortex with the same latency and were perceived as simultaneous, but this timing perception was highly sensitive to adaption. Conclusions: VI improves oculomotor, postural, and perceptual behavior in vestibulopathic monkeys and could prove to be an effective way to improve these functions in patients with permanent labyrinthine damage. Timing experiments show that when novel stimuli are used, the brain synthesizes them in accordance with their arrival at the cortex, but that experience can rapidly recalibrate this timing relationship, which may be why normal stimuli that are experienced habitually lack this characteristic. 

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4. Exploring Multidimensional Measurements for Pain Evaluation using Facial Action Units

   https://doi.org/10.1109/FG47880.2020.00087  

   Xu, Xiaojing ; de Sa, Virginia R. ( January 2020 , 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020))

   Although pain is widely recognized to be a multidimensional experience, it is typically measured by unidimensional patient self-reported visual analog scale (VAS). However, self-reported pain is subjective, difficult to interpret and sometimes impossible to obtain. Machine learning models have been developed to automatically recognize pain at both the frame level and sequence (or video) level. Many methods use or learn facial action units (AUs) defined by the Facial Action Coding System (FACS) for describing facial expressions with muscle movement. In this paper, we analyze the relationship between sequence-level multidimensional pain measurements and frame-level AUs and an AU derived pain-related measure, the Prkachin and Solomon Pain Intensity (PSPI). We study methods that learn sequence-level metrics from frame-level metrics. Specifically, we explore an extended multitask learning model to predict VAS from human-labeled AUs with the help of other sequence-level pain measurements during training. This model consists of two parts: a multitask learning neural network model to predict multidimensional pain scores, and an ensemble learning model to linearly combine the multidimensional pain scores to best approximate VAS. Starting from human-labeled AUs, the model achieves a mean absolute error (MAE) on VAS of 1.73. It outperforms provided human sequence-level estimates which have an MAE of 1.76. Combining our machine learning model with the human estimates gives the best performance of MAE on VAS of 1.48. 

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5. Effects of transcutaneous spinal stimulation on spatiotemporal cortical activation patterns: a proof-of-concept EEG study

   https://doi.org/10.1088/1741-2552/ac7b4b  

   Steele, Alexander G ; Manson, Gerome A ; Horner, Philip J ; Sayenko, Dimitry G ; Contreras-Vidal, Jose L ( July 2022 , Journal of Neural Engineering)

   Abstract Objective. Transcutaneous spinal cord stimulation (TSS) has been shown to be a promising non-invasive alternative to epidural spinal cord stimulation for improving outcomes of people with spinal cord injury (SCI). However, studies on the effects of TSS on cortical activation are limited. Our objectives were to evaluate the spatiotemporal effects of TSS on brain activity, and determine changes in functional connectivity under several different stimulation conditions. As a control, we also assessed the effects of functional electrical stimulation (FES) on cortical activity. Approach . Non-invasive scalp electroencephalography (EEG) was recorded during TSS or FES while five neurologically intact participants performed one of three lower-limb tasks while in the supine position: (1) A no contraction control task, (2) a rhythmic contraction task, or (3) a tonic contraction task. After EEG denoising and segmentation, independent components (ICs) were clustered across subjects to characterize sensorimotor networks in the time and frequency domains. ICs of the event related potentials (ERPs) were calculated for each cluster and condition. Next, a Generalized Partial Directed Coherence (gPDC) analysis was performed on each cluster to compare the functional connectivity between conditions and tasks. Main results . IC analysis of EEG during TSS resulted in three clusters identified at Brodmann areas (BA) 9, BA 6, and BA 4, which are areas associated with working memory, planning, and movement control. Lastly, we found significant ( p  < 0.05, adjusted for multiple comparisons) increases and decreases in functional connectivity of clusters during TSS, but not during FES when compared to the no stimulation conditions. Significance. The findings from this study provide evidence of how TSS recruits cortical networks during tonic and rhythmic lower limb movements. These results have implications for the development of spinal cord-based computer interfaces, and the design of neural stimulation devices for the treatment of pain and sensorimotor deficit. 

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