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1. Facile Functionalization of Graphene to Tune Response of Printed Electrochemical Sensors to Neurotransmitters

   https://doi.org/10.1149/MA2023-01532651mtgabs  

   Kammarchedu, Vinay ; Butler, Derrick ; Khamsi, Pouya Soltan ; Ebrahimi, Aida ( August 2023 , ECS Meeting Abstracts)

   Neurotransmitters are small molecules involved in neuronal signaling and can also serve as stress biomarkers.¹Their abnormal levels have been also proposed to be indicative of several neurological diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington disease, among others. Hence, measuring their levels is highly important for early diagnosis, therapy, and disease prognosis. In this work, we investigate facile functionalization methods to tune and enhance sensitivity of printed graphene sensors to neurotransmitters. Sensors based on direct laser scribing and screen-printed graphene ink are studied. These printing methods offer ease of prototyping and scalable fabrication at low cost.

   The effect of functionalization of laser induced graphene (LIG) by electrodeposition and solution-based deposition of TMDs (molybdenum disulfide²and tungsten disulfide) and metal nanoparticles is studied. For different processing methods, electrochemical characteristics (such as electrochemically active surface area: ECSA and heterogenous electron transfer rate: k⁰) are extracted and correlated to surface chemistry and defect density obtained respectively using X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy. These functionalization methods are observed to directly impact the sensitivity and limit of detection (LOD) of the graphene sensors for the studied neurotransmitters. For example, as compared to bare LIG, it is observed that electrodeposition of MoS₂on LIG improves ECSA by 3 times and k⁰by 1.5 times.³Electrodeposition of MoS₂also significantly reduces LOD of serotonin and dopamine in saliva, enabling detection of their physiologically relevant concentrations (in pM-nM range). In addition, chemical treatment of LIG sensors is carried out in the form of acetic acid treatment. Acetic acid treatment has been shown previously to improve C-C bonds improving the conductivity of LIG sensors.⁴In our work, in particular, acetic acid treatment leads to larger improvement of LOD of norepinephrine compared to MoS₂electrodeposition.

   In addition, we investigate the effect of plasma treatment to tune the sensor response by modifying the defect density and chemistry. For example, we find that oxygen plasma treatment of screen-printed graphene ink greatly improves LOD of norepinephrine up to three orders of magnitude, which may be attributed to the increased defects and oxygen functional groups on the surface as evident by XPS measurements. Defects are known to play a key role in enhancing the sensitivity of 2D materials to surface interactions, and have been explored in tuning/enhancing the sensor sensitivity.⁵Building on our previous work,³we apply a custom machine learning-based data processing method to further improve that sensitivity and LOD, and also to automatically benchmark different molecule-material pairs.

   Future work includes expanding the plasma chemistry and conditions, studying the effect of precursor mixture in laser-induced solution-based functionalization, and understanding the interplay between molecule-material system. Work is also underway to improve the machine learning model by using nonlinear learning models such as neural networks to improve the sensor sensitivity, selectivity, and robustness.

   References

   A. J. Steckl, P. Ray, (2018), doi:10.1021/acssensors.8b00726.

   Y. Lei, D. Butler, M. C. Lucking, F. Zhang, T. Xia, K. Fujisawa, T. Granzier-Nakajima, R. Cruz-Silva, M. Endo, H. Terrones, M. Terrones, A. Ebrahimi,Sci. Adv.6, 4250–4257 (2020).

   V. Kammarchedu, D. Butler, A. Ebrahimi,Anal. Chim. Acta.1232, 340447 (2022).

   H. Yoon, J. Nah, H. Kim, S. Ko, M. Sharifuzzaman, S. C. Barman, X. Xuan, J. Kim, J. Y. Park,Sensors Actuators B Chem.311, 127866 (2020).

   T. Wu, A. Alharbi, R. Kiani, D. Shahrjerdi,Adv. Mater.31, 1–12 (2019).

    

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2. 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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3. Identifying the granitic composition water-saturated solidus

   Rufledt C, Thomas JB ( September 2023 , Xth Hutton Symposium)

   The granitic water-saturated solidus (G-WSS) is the lower temperature limit of magmatic mineral crystallization. The accepted water-saturated solidus for granitic compositions was largely determined >60 years ago1. More recent advances in experimental petrology, improved analytical techniques, and recent observations that granitic systems can remain active or spend a significant proportion of their lives at conditions below the traditional G-WSS2–5 necessitate a careful experimental investigation of the near-solidus regions of granitic systems. Natural and synthetic starting materials were melted at 10 kbar and 900°C with 48 wt% H2O to produce hydrous glasses for subsequent experiments at lower PT conditions used to locate the G-WSS. We performed crystallization experiments and melting experiments at temperatures ranging from 575 to 800°C and 1, 6, 8, and 10 kbar on 12 granitoid compositions. First, we ran a series of isothermal crystallization experiments along each isobar at progressively lower temperatures until runs completely crystallized to identify apparent solidus temperatures. Geochemical analyses of quenched glass compositions demonstrate that progressive crystallization drives all starting compositions towards silica-rich, water-saturated rhyolitic/granitic melts (e.g., ~7578 wt% SiO2). After identifying the apparent solidus temperatures at which the various compositions crystallized, we then ran series of reversal-type melting experiments. With the goal of producing rocks with hydrous equilibrium microstructures, we crystallized compositions at temperatures ~10°C below the apparent solidus identified in crystallization experiments, and then heated isobarically to conditions that produced ~20% melt during the crystallization experiments. Importantly, crystallization experiments and heating experiments at the same PT conditions produced similar proportions of melt, crystals, and vapor. A time-series of experiments 230 days at PT conditions previously identified to produce ~10% to 20% melt did not reveal any kinetic effects on melt crystallization. Experiments at 6 to 10 kbar crystallized/melted at temperatures close to the published G-WSS. However, at lower pressures where the published G-WSS is strongly curved in PT space, all compositions investigated contained melt to temperatures ~75 to 100°C below the accepted G-WSS. The similarity of crystallization temperatures for the higher-pressure experiments to previously published results, similar phase proportions in melting and crystallization experiments, and the lack of kinetic effects on crystallization collectively suggest that our lower pressure constraints on the G-WSS are accurate. The new experimental results demonstrating that the lower-pressure G-WSS is significantly lower than unanimously accepted estimates will help us to better understand the storage conditions, evolution, and potential for eruption in mid- to upper-crustal silicic magmatic systems. (1) Tuttle, O.; Bowen, N. Origin of Granite in the Light of Experimental Studies in the System NaAlSi3O8–KAlSi3O8–SiO2–H2O; Geological Society of America Memoirs; Geological Society of America, 1958; Vol. 74. https://doi.org/10.1130/MEM74. (2) Rubin, A. E.; Cooper, K. M.; Till, C. B.; Kent, A. J. R.; Costa, F.; Bose, M.; Gravley, D.; Deering, C.; Cole, J. Rapid Cooling and Cold Storage in a Silicic Magma Reservoir Recorded in Individual Crystals. Science 2017, 356 (6343), 1154–1156. https://doi.org/10.1126/science.aam8720. (3) Andersen, N. L.; Jicha, B. R.; Singer, B. S.; Hildreth, W. Incremental Heating of Bishop Tuff Sanidine Reveals Preeruptive Radiogenic Ar and Rapid Remobilization from Cold Storage. Proceedings of the National Academy of Sciences 2017, 114 (47), 12407–12412. https://doi.org/10.1073/pnas.1709581114. (4) Ackerson, M. R.; Mysen, B. O.; Tailby, N. D.; Watson, E. B. Low-Temperature Crystallization of Granites and the Implications for Crustal Magmatism. Nature 2018, 559 (7712), 94–97. https://doi.org/10.1038/s41586-018-0264-2. (5) Glazner, A. F.; Bartley, J. M.; Coleman, D. S.; Lindgren, K. Aplite Diking and Infiltration: A Differentiation Mechanism Restricted to Plutonic Rocks. Contributions to Mineralogy and Petrology 2020, 175 (4). https://doi.org/10.1007/s00410-020-01677-1. 

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4. L oo p D e l t a : E m b e dd i n g L o c a li t y - a w a r e O pp o r t un i s t i c D e l t a C o m p r e ss i o n i n I n li n e D e dup li c a t i o n f o r Hi g h l y E ffic i e n t D a t a R e du c t i o n

   Zhang, Yucheng ; Jiang, Hong ; Feng, Dan ; Jiang Nan ; Qui, Taorong ; Huang, Wei ( July 2023 , USENIX annual technical conference)

   A s a c om pl e men t t o da ta d edupli cat ion , de lta c om p ress i on fu r- t he r r edu c es t h e dat a vo l u m e by c o m pr e ssi n g n o n - dup li c a t e d ata chunk s r e l a t iv e to t h e i r s i m il a r chunk s (bas e chunk s). H ow ever, ex is t i n g p o s t - d e dup li c a t i o n d e l t a c o m pr e ssi o n a p- p ro a ches fo r bac kup s t or ag e e i t h e r su ffe r f ro m t h e l ow s i m - il a r i t y b e twee n m any de te c ted c hun ks o r m i ss so me po t e n - t i a l s i m il a r c hunks , o r su ffer f r om l ow (ba ckup and r es t ore ) th r oug hpu t du e t o extr a I/ Os f or r e a d i n g b a se c hun ks o r a dd a dd i t i on a l s e r v i c e - d i s r up t ive op e r a t i on s to b a ck up s ys t em s. I n t h i s pa p e r, w e pr opo se L oop D e l t a t o a dd ress the above - m e n t i on e d prob l e m s by an e nha nced em b e ddi n g d e l t a c o m p - r e ss i on sc heme i n d e dup li c a t i on i n a non - i n t ru s ive way. T h e e nha nce d d elt a c o mpr ess ion s che m e co m b in e s f our key t e c h - ni qu e s : (1) du a l - l o c a li t y - b a s e d s i m il a r i t y t r a c k i n g to d e t ect po t e n t i a l si m il a r chun k s b y e x p l o i t i n g both l o g i c a l and ph y - s i c a l l o c a li t y, ( 2 ) l o c a li t y - a wa r e pr e f e t c h i n g to pr efe tc h ba se c hun ks to a vo i d ex t ra I/ Os fo r r e a d i n g ba s e chun ks on t h e w r i t e p at h , (3) c a che -aware fil t e r to avo i d ext r a I/Os f or b a se c hunk s on t he read p at h, a nd (4) i nver sed de l ta co mpressi on t o perf orm de lt a co mpress i o n fo r d at a chunk s t hat a re o th e r wi se f o r b i dd e n to s er ve as ba se c hunk s by r ew r i t i n g t e c hn i qu e s d e s i g n e d t o i m p r ove r es t o re pe rf o rma nc e. E x p e r i m e n t a l re su lts indi ca te t hat L oop D e l t a i ncr ea se s t he c o m pr e ss i o n r a t i o by 1 .2410 .97 t i m e s on t op of d e dup li c a - t i on , wi t hou t no t a b l y a ffe c t i n g th e ba ck up th rou ghpu t, a nd i t i m p r ove s t he res to re p er fo r m an ce b y 1.23.57 t i m e 

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5. Individual differences and long-term consequences of tDCS augmented cognitive training

   Katz, B. ; Au, J. ; Buschkuehl, M. ; Abagis, T. ; Zabel, C. ; Jaeggi, S. ; Jonides, J. ( October 2017 , Journal of cognitive neuroscience)

   A gr e at d e al of i nt er e st s urr o u n d s t h e u s e of tr a n s cr a ni al dir e ct c urr e nt sti m ul ati o n (t D C S) t o a u g m e nt c o g niti v e tr ai ni n g. H o w e v er, eff e ct s ar e i n c o n si st e nt a cr o s s st u di e s, a n d m et aa n al yti c e vi d e n c e i s mi x e d, e s p e ci all y f o r h e alt h y, y o u n g a d ult s. O n e m aj or s o ur c e of t hi s i n c o n si st e n c y i s i n di vi d u al diff er e n c e s a m o n g t h e p arti ci p a nt s, b ut t h e s e diff er e n c e s ar e r ar el y e x a mi n e d i n t h e c o nt e xt of c o m bi n e d tr ai ni n g/ sti m ul ati o n st u di e s. I n a d diti o n, it i s u n cl e ar h o w l o n g t h e eff e ct s of sti m ul ati o n l a st, e v e n i n s u c c e s sf ul i nt er v e nti o n s. S o m e st u di e s m a k e u s e of f oll o w- u p a s s e s s m e nt s, b ut v er y f e w h a v e m e a s ur e d p erf or m a n c e m or e t h a n a f e w m o nt hs aft er a n i nt er v e nti o n. H er e, w e utili z e d d at a fr o m a pr e vi o u s st u d y of t D C S a n d c o g niti v e tr ai ni n g [ A u, J., K at z, B., B u s c h k u e hl, M., B u n arj o, K., S e n g er, T., Z a b el, C., et al. E n h a n ci n g w or ki n g m e m or y tr ai ni n g wit h tr a n scr a ni al dir e ct c urr e nt sti m ul ati o n. J o u r n al of C o g niti v e N e u r os ci e n c e, 2 8, 1 4 1 9 – 1 4 3 2, 2 0 1 6] i n w hi c h p arti ci p a nts tr ai n e d o n a w or ki n g m e m or y t as k o v er 7 d a y s w hil e r e c ei vi n g a cti v e or s h a m t D C S. A n e w, l o n g er-t er m f oll o w- u p t o a ss es s l at er p erf or m a n c e w a s c o n d u ct e d, a n d a d diti o n al p arti ci p a nt s w er e a d d e d s o t h at t h e s h a m c o n diti o n w a s b ett er p o w er e d. W e a s s e s s e d b a s eli n e c o g niti v e a bilit y, g e n d er, tr ai ni n g sit e, a n d m oti v ati o n l e v el a n d f o u n d si g nifi c a nt i nt er a cti o ns b et w e e n b ot h b as eli n e a bilit y a n d m oti v ati o n wit h c o n diti o n ( a cti v e or s h a m) i n m o d els pr e di cti n g tr ai ni n g g ai n. I n a d diti o n, t h e i m pr o v e m e nt s i n t h e a cti v e c o nditi o n v er s u s s h a m c o n diti o n a p p e ar t o b e st a bl e e v e n a s l o n g a s a y e ar aft er t h e ori gi n al i nt er v e nti o n. ■ 

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