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1. The Influence of Absolute Mass Loading of Secondary Organic Aerosols on Their Phase State

   https://doi.org/10.3390/atmos9040131  

   S.Jain, K.B. Fischer ( April 2018 , Atmosphere)

   Absolute secondary organic aerosol (SOA) mass loading (CSOA) is a key parameter in determining partitioning of semi- and intermediate volatility compounds to the particle phase. Its impact on the phase state of SOA, however, has remained largely unexplored. In this study, systematic laboratory chamber measurements were performed to elucidate the influence of CSOA, ranging from 0.2 to 160 µg m−3, on the phase state of SOA formed by ozonolysis of various precursors, including α-pinene, limonene, cis-3-hexenyl acetate (CHA) and cis-3-hexen-1-ol (HXL). A previously established method to estimate SOA bounce factor (BF, a surrogate for particle viscosity) was utilized to infer particle viscosity as a function of CSOA. Results show that under nominally identical conditions, the maximum BF decreases by approximately 30% at higher CSOA, suggesting a more liquid phase state. With the exception of HXL-SOA (which acted as the negative control), the phase state for all studied SOA precursors varied as a function of CSOA. Furthermore, the BF was found to be the maximum when SOA particle distributions reached a geometric mean particle diameter of 50–60 nm. Experimental results indicate that CSOA is an important parameter impacting the phase state of SOA, reinforcing recent findings that extrapolation of experiments not conducted at atmospherically relevant SOA levels may not yield results that are relevant to the natural environment. 

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2. Predicting the glass transition temperature and viscosity of secondary organic material using molecular composition

   https://doi.org/10.5194/acp-18-6331-2018  

   DeRieux, Wing-Sy Wong ; Li, Ying ; Lin, Peng ; Laskin, Julia ; Laskin, Alexander ; Bertram, Allan K. ; Nizkorodov, Sergey A. ; Shiraiwa, Manabu ( January 2018 , Atmospheric Chemistry and Physics)

   Secondary organic aerosol (SOA) accounts for a large fraction of submicron particles in the atmosphere. SOA can occur in amorphous solid or semi-solid phase states depending on chemical composition, relative humidity (RH), and temperature. The phase transition between amorphous solid and semi-solid states occurs at the glass transition temperature (T_g). We have recently developed a method to estimate T_g of pure compounds containing carbon, hydrogen, and oxygen atoms (CHO compounds) with molar mass less than 450 g mol⁻¹ based on their molar mass and atomic O : C ratio. In this study, we refine and extend this method for CH and CHO compounds with molar mass up to ∼ 1100 g mol⁻¹ using the number of carbon, hydrogen, and oxygen atoms. We predict viscosity from the T_g-scaled Arrhenius plot of fragility (viscosity vs. T_g∕T) as a function of the fragility parameter D. We compiled D values of organic compounds from the literature and found that D approaches a lower limit of ∼ 10 (±1.7) as the molar mass increases. We estimated the viscosity of α-pinene and isoprene SOA as a function of RH by accounting for the hygroscopic growth of SOA and applying the Gordon–Taylor mixing rule, reproducing previously published experimental measurements very well. Sensitivity studies were conducted to evaluate impacts of T_g, D, the hygroscopicity parameter (κ), and the Gordon–Taylor constant on viscosity predictions. The viscosity of toluene SOA was predicted using the elemental composition obtained by high-resolution mass spectrometry (HRMS), resulting in a good agreement with the measured viscosity. We also estimated the viscosity of biomass burning particles using the chemical composition measured by HRMS with two different ionization techniques: electrospray ionization (ESI) and atmospheric pressure photoionization (APPI). Due to differences in detected organic compounds and signal intensity, predicted viscosities at low RH based on ESI and APPI measurements differ by 2–5 orders of magnitude. Complementary measurements of viscosity and chemical composition are desired to further constrain RH-dependent viscosity in future studies. 

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3. Predictions of the glass transition temperature and viscosity of organic aerosols from volatility distributions

   https://doi.org/10.5194/acp-20-8103-2020  

   Li, Ying ; Day, Douglas A. ; Stark, Harald ; Jimenez, Jose L. ; Shiraiwa, Manabu ( January 2020 , Atmospheric Chemistry and Physics)

   Abstract. Volatility and viscosity are important properties of organic aerosols (OA),affecting aerosol processes such as formation, evolution, and partitioning ofOA. Volatility distributions of ambient OA particles have often beenmeasured, while viscosity measurements are scarce. We have previouslydeveloped a method to estimate the glass transition temperature (Tg) ofan organic compound containing carbon, hydrogen, and oxygen. Based onanalysis of over 2400 organic compounds including oxygenated organiccompounds, as well as nitrogen- and sulfur-containing organic compounds, weextend this method to include nitrogen- and sulfur-containing compoundsbased on elemental composition. In addition, parameterizations are developedto predict Tg as a function of volatility and the atomicoxygen-to-carbon ratio based on a negative correlation between Tg andvolatility. This prediction method of Tg is applied to ambientobservations of volatility distributions at 11 field sites. Thepredicted Tg values of OA under dry conditions vary mainly from 290 to 339 Kand the predicted viscosities are consistent with the results of ambientparticle-phase-state measurements in the southeastern US and the Amazonianrain forest. Reducing the uncertainties in measured volatility distributionswould improve predictions of viscosity, especially at low relative humidity.We also predict the Tg of OA components identified via positive matrixfactorization of aerosol mass spectrometer (AMS) data. The predicted viscosity ofoxidized OA is consistent with previously reported viscosity of secondary organic aerosols (SOA) derivedfrom α-pinene, toluene, isoprene epoxydiol (IEPOX), and diesel fuel.Comparison of the predicted viscosity based on the observed volatilitydistributions with the viscosity simulated by a chemical transport modelimplies that missing low volatility compounds in a global model can lead tounderestimation of OA viscosity at some sites. The relation betweenvolatility and viscosity can be applied in the molecular corridor orvolatility basis set approaches to improve OA simulations in chemicaltransport models by consideration of effects of particle viscosity in OAformation and evolution. 

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4. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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5. Estimation of secondary organic aerosol viscosity from explicit modeling of gas-phase oxidation of isoprene and <i>α</i>-pinene

   https://doi.org/10.5194/acp-21-10199-2021  

   Galeazzo, Tommaso ; Valorso, Richard ; Li, Ying ; Camredon, Marie ; Aumont, Bernard ; Shiraiwa, Manabu ( January 2021 , Atmospheric Chemistry and Physics)

   Abstract. Secondary organic aerosols (SOA) are major components of atmospheric fineparticulate matter, affecting climate and air quality. Mounting evidenceexists that SOA can adopt glassy and viscous semisolid states, impactingformation and partitioning of SOA. In this study, we apply the GECKO-A(Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere)model to conduct explicit chemical modeling of isoprene photooxidation andα-pinene ozonolysis and their subsequent SOA formation. The detailedgas-phase chemical schemes from GECKO-A are implemented into a box model andcoupled to our recently developed glass transition temperatureparameterizations, allowing us to predict SOA viscosity. The effects ofchemical composition, relative humidity, mass loadings and mass accommodation on particle viscosity are investigated in comparison withmeasurements of SOA viscosity. The simulated viscosity of isoprene SOAagrees well with viscosity measurements as a function of relative humidity,while the model underestimates viscosity of α-pinene SOA by a feworders of magnitude. This difference may be due to missing processes in themodel, including autoxidation and particle-phase reactions, leading to theformation of high-molar-mass compounds that would increase particleviscosity. Additional simulations imply that kinetic limitations of bulkdiffusion and reduction in mass accommodation coefficient may play a role inenhancing particle viscosity by suppressing condensation of semi-volatilecompounds. The developed model is a useful tool for analysis andinvestigation of the interplay among gas-phase reactions, particle chemicalcomposition and SOA phase state. 

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