Conventional Time Research Articles

Crosswell frequency-domain reverse time migration imaging with wavefield decomposition

Abstract Crosswell seismic technique can provide high-resolution imaging and monitoring of the subsurface. However, compared to the surface seismic, crosswell data contain more complex wave components, which increases the difficulty of seismic processing and migrations. Conventional acoustic reverse time migration (RTM) mainly uses the cross-correlation of the source forward and the receiver backward wavefields. The redundant information generated by cross-correlation may undermine the imaging reliability in the crosswell models. Thus, we develop a novel wavefield decomposition imaging condition and only used cross-correlation information of incident and reflection waves in the same propagation directions, which eliminates the artifacts generated from the cross-correlation of wave information unrelated to reflections in the crosswell image. We perform RTM in the frequency domain to maintain efficiency in multi-shot problems. A mono-frequency wavefield decomposition method is applied to separate and process the seismic data. The forward and backward wavefields are reclassified into the up- and downpropagating components. The L1 norm is introduced to enhance the robustness of the proposed imaging method. We then use this method to synthesize data from layering models and analyze the imaging results generated from each pair of cross-correlations using source and receiver wavefields. Results show that the cross-correlation information belonging to the same propagation contributes most to the crosswell image, and the other information always generates migration noises. Moreover, we apply the proposed method to a real-field dataset. Processing results validate the effectiveness of the proposed means for eliminating false events in the crosswell models and improving image quality.

Multiparameter least‐squares reverse time migration using the viscoacoustic‐wave equation

AbstractIn viscoacoustic least‐squares reverse time migration methods, the reflectivity image associated with the Q factor is negligible, inverting only the velocity (v) parameter or v‐related variables such as squared slowness or bulk modulus. However, the Q factor influences the amplitude and phase of the seismic data, especially in basins containing gas reservoirs or storing . Therefore, the Q factor and its associated parameters must be considered in the context of viscoacoustic least‐squares reverse time migration. Thus, we propose a multiparameter viscoacoustic least‐squares reverse time migration procedure, which obtains the inverse of bulk modulus (κ) and the Q magnitude (τ) simultaneously. We derive and implement the multiparameter forward and adjoint pair Born operators and the gradient formulas concerning κ and τ parameters. Then, we apply these derivations in our proposed multiparameter approach, which can produce images with better balanced amplitudes and more resolution than conventional reverse time migration images.

Criticality indices provided by the evolution of pressure stimulated currents and acoustic emissions in the natural time domain

The target of this study is to detect the existence of criticality indices, namely, indices which may be considered as in-time warning signals about the entrance of a mechanically loaded system into its critical stage, i.e., the stage of impending fracture. To achieve this target advantage is taken of the experimental data provided by two sensing techniques, namely the Acoustic Emissions and the Pressure Stimulated Currents ones. The innovation of the present study is that the temporal evolution of the acoustic and electrical activities is implemented in terms of the Natural Time rather than in terms of the conventional time. The experimental protocol included three-point bending tests with centrally notched beam-shaped specimens made of either plain concrete or concrete reinforced with either plastic or metallic short fibers. The comparative analysis of the acoustic and electric activities in the Natural Time Domain revealed that both techniques provide clearly distinguishable criticality indices. Moreover, it was proven that the overall evolution of the acoustic activity is quite compatible with the respective one of the electric activity, concerning both the instant at which the pre-failure indices are detected, and, also, the duration of the respective regions of criticality.

Ultrasound-assisted extraction of Sn from tinplate scraps by alkaline leaching: Novel acoustoelectric synergy effect underlying intensifying mechanism

Clean and fast extraction of tin from the surface of tinplate scraps is of great significance for the efficient utilization of waste resources. However, the dense tin layer causes the low efficiency of conventional leaching process. To improve Sn leaching efficiency, the ultrasound technique was adopted to extract Sn from tinplate scraps by alkaline leaching in this study. In the NaOH-H2O2 leaching system, metallic tin and alloyed tin in Fe-Sn alloy located on the surface of tinplate scraps can be oxidized and transferred to soluble Na2SnO3, while the iron in Fe-Sn alloy was oxidized to oxides which were chemically inert in alkaline solution. The differences in chemical solubility of Sn and Fe, and solubleness of stannate and iron oxides gave rise to the selective separation of Sn from the tinplate scraps. The effects of the leaching parameters on the Sn leaching behaviors in conventional and ultrasound-assisted leaching processes were compared. The conventional leaching temperature and time were significantly reduced during the ultrasound-assisted leaching process. Almost all of Sn can be extracted after conventional leaching at 1 mol/L NaOH, temperature of 80 ℃ and time of 60 min, however the same Sn leaching effect can be achieved by ultrasound-assisted leaching at 60 ℃ for 30 min with ultrasound power of 60% (360 W). Sn leaching kinetics based on the plate model demonstrated the reaction rate constant of the ultrasound-assisted leaching was 70% higher than that of the conventional leaching. A novel acoustoelectric synergy effect underlying intensifying mechanism by ultrasound irradiation was proposed in this study. Eventually, this work provided a rapid and clean tin extraction method from tinplate scraps via the ultrasound-assisted alkaline leaching treatment.

OR30-03 High Resolution Daily Profiles Of Tissue Adrenal Steroids By Portable Automated Collection

Abstract Disclosure: T. Upton: None. E. Zavala: None. P. Methlie: None. M. Oksnes: None. M.A. Grytaas: None. K. Simunkova: None. S. Bensing: None. I. Botusan: None. K. Berinder: None. D.A. Vassiliadi: None. O. Kampe: None. S. Tsagarakis: None. E.S. Husebye: None. G.M. Russell: None. S.L. Lightman: None. Background: Rhythms are intrinsic to endocrine systems, and disruption of these hormone oscillations occurs at very early stages of disease. Since adrenal hormones are secreted with both circadian and ultradian periods, conventional single time point measures provide limited information about rhythmicity, and crucially do not provide information during sleep when many hormones fluctuate from nadir to peak concentrations. If blood sampling is attempted overnight this necessitates admission to a clinical research unit, can be stressful and disturbs normal sleep. Aim: To obtain automated ambulatory 24-hour profiles of adrenal steroids in healthy human subcutaneous tissue to characterize healthy variability without blood sampling. Methods: We used microdialysis, an ambulatory fraction collector and liquid-chromatography mass-spectrometry to obtain 72-point profiles of tissue free adrenal steroids over 24 hours in n=214 healthy volunteers. For validation, we compared tissue against plasma measurements in n=7 inpatient healthy volunteers. We used stationery statistics, time series analyses, and mathematical methods to characterise interindividual and intraindividual variability of these hormones at different times of the day. Results: There were no serious adverse events, and the method was well tolerated, allowing almost all normal activities to continue. In addition to cortisol, we identified daily and ultradian variation in free cortisone, corticosterone, 18-OHF, aldosterone, THF, and aTHF. Rhythmic hormones were highly correlated when measured across the day, especially cortisol with 18OHF (rs = 0.99), cortisol with cortisone (rs = 0.96) and cortisol and 18-OHF with corticosterone (rs = 0.96-0.97). There was a very strong correlation between mean values of cortisol and 18OHF with aldosterone (rs = 0.91-0.93), but aldosterone profiles were characterized by high interindividual variability. Tissue 18-OHF was the most highly correlated with plasma cortisol (rs 0.89, range 0.74—0.97, p<0.001), and 18-OHF phase was advanced with respect to tissue cortisol. The ratio of tissue cortisol:cortisone exhibited a stronger daily rhythm than in plasma, with cortisol concentrations often falling below cortisone in the late afternoon and early part of sleep.Using the data, we computed mathematically defined metrics (“dynamic biomarkers”) stratified by sex, age, and BMI to quantify individual variability of hormone rhythms. Provisional analysis suggests no evidence for an effect of sex on cortisol or 18OHF, but evidence for a small effect of BMI on total 24-hour cortisol, 18OHF, corticosterone, and aldosterone. Conclusions: These data provide, for the first time, an insight into the dynamics of adrenal steroids in tissue in real world setting and will serve normative reference data for establishing novel and improved biomarkers of endocrine disease. Presentation: Sunday, June 18, 2023

Timing Synchronization Based on Supervised Learning of Spectrogram for OFDM Systems

This paper proposes a supervised convolutional neural network (CNN) based symbol timing synchronization method using the spectrogram image for preamble-less orthogonal frequency division multiplexing (OFDM) systems. With the development of mobile terminals, OFDM has become an increasingly widespread fundamental technology for wireless communications. While OFDM can achieve high-speed transmission, it is sensitive to synchronization timing for decoding. Thus, the accurate synchronization timing estimation method has become essential for reliable communication. Conventional synchronization timing estimation methods without the preamble lack investigations of estimation accuracy under varying environments, comprehensive performance evaluation, and robustness to Doppler shift. Focusing on the spectrum fluctuations observed when synchronization errors occur, our proposed approach is to train the CNN using spectrogram images to find accurate synchronization points even in noisy and fluctuating environments. The simulation results show that the proposed method achieves better synchronization accuracy than other existing methods. Furthermore, it shows the near-optimal bit error rate (BER) characteristics and superior processing time for BER in an environment close to realistic settings, such as broader synchronization timing and various Doppler shifts.

A Comparison between Thromboelastography and Conventional Clotting Tests in Pediatric Patients After Cardiopulmonary Bypass Procedure.

The coagulation system is more complicated in younger infants because the hemostatic system is not completely mature before 6 months. There is confusion among pediatricians to choose conventional coagulation tests and thromboelastography (TEG) to evaluate coagulation function for infants in major surgery. This study was undertaken to perform a comparison between the two methods for pediatric patients who underwent cardio-pulmonary bypass (CPB) surgery. Infant patients who underwent CPB surgery were divided into two groups - younger group (age < 6 months old, n = 72) and older group (age from 6 months old to 12 years old, n = 76). Prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (Fib) of conventional coagulation tests and reaction time (R-time), speed of fibrin building up (ɑ-Angle), clot conformation time (K-time), maximum colt amplitude (MA) of TEG results before and after CPB, as well as increasing or decreasing rate of all the values after CPB, were compared between the two groups. Postoperative transfusion details were summarized. PT, APTT, R-time, and K-time markedly increased (p < 0.05) and Fib, ɑ-Angle, and MA decreased (p < 0.05) after CPB in both groups. The younger group had a much higher rate of postoperative transfusion with suspended red cells (54.17% vs. 17.11%), fresh frozen plasma (29.17% vs. 9.21%), cryoprecipitate (9.72% vs. 1.32%), and apheresis platelet (5.56% vs. 0) than the older group. Increasing rate of R-time and K-time and decreasing rate of ɑ-Angle and MA after CPB in the younger group were significantly higher than that in the older group (p < 0.01), whereas no significant differences were observed in conventional coagulation tests (p > 0.2). TEG was more sensitive than conventional coagulation tests in response to variation of coagulation function for younger infants after major surgery.

Nicodemus—A Rabbi Seeking Wisdom

The meeting of two teachers at night suggests that they will be discussing sacred texts, as this was a conventional time for such learned dialogues. Nicodemus initiates the discussion, pointing to the “signs” Jesus has performed; Jesus elaborates further on the deeper meaning of the signs that Nicodemus so far has missed. The story of Jacob in Genesis 28 and the descriptions in the Wisdom of Solomon provide rich intertext for understanding the Nicodemus pericope. In the person of Jesus, the Son of Man has descended from heaven, and the heavens have been opened, enabling access to the reign of God for the one who is born anew.

Evaluation of Multiweek Tropical Cyclone Forecasts in the Philippines

Abstract In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional time scales covered by weather forecasting in the Philippines, this study has examined the multiweek (i.e., from week 1 to week 4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely, the NCEP Climate Forecast System version 2 (CFSv2), the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and the NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 6 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multiweek TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p &lt; 0.05) positional bias toward the right of observed tracks beyond week 1, and that they tend to propagate slower than observations especially in week 1 and week 2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multiweek TC forecasts in the Philippines.

Active learning based reverse design of hydrogen production from biomass fuel

Biomass hydrogen production holds substantial promise in addressing critical challenges within the realms of renewable energy and environmental sustainability. Nevertheless, the labor- and time-intensive nature of conducting combinatorial experiments and relying on traditional trial-and-error methods significantly constrains the exploration of biomass fuel solutions. This paper introduces a novel framework for the reverse engineering of hydrogen production systems, which seamlessly integrates Bayesian active learning with machine learning models. In Bayesian active learning, 307 biomass fuel samples were expanded to 1354 in order to ensure the accuracy of the model prediction between the seven characteristics of biomass fuel and hydrogen production. Four machine learning algorithms including RF, XGBoost, KNNR, AdaBoost were used to build the prediction model. A stochastic search reverse design approach based on the XGBoost model with the highest coefficient of determination (R2 = 0.91) was innovatively used to realize the “reverse design” from “target performance” to “composition and process”. Through reverse design, a two-order-of-magnitude increase in design space is achieved in hundreds of times less time than conventional design time, resulting in a significant improvement in hydrogen production efficiency. The practical application of this method for hydrogen production from biomass fuels may go far beyond linear extrapolation based on the current state of the art and can be applied to a wider range of material designs.

Reperfusion Therapies in Acute Ischemic Stroke Beyond the Conventional Time Window: A Narrative Review.

Stroke is the second most common cause of death worldwide, with 50% of survivors experiencing long-term disability. For more than two decades, treatment with intravenous thrombolysis (IVT) and mechanical endovascular thrombectomy (MET), the only approved stroke reperfusion therapies, was restricted to patients within the 4.5-6 hour time window, respectively. Therefore, patients who presented with acute ischemic stroke (AIS) beyond the conventionaltime window were excluded from reperfusion treatment. This narrative review aims to review the scientific literature on the possibilities of reperfusion therapies for patients who present with an unknown time of stroke onset, and those with stroke onset beyond the conventional 4.5-6 hour time window. Beyond the conventional time window, the eligibility of patients for IVT or MET, the two main therapeutic procedures, is decided based on the concept of penumbral imaging. Penumbral imaging identifies patients with hypoperfused but viable brain tissue, who could benefit from reperfusion. On the other hand, clock-based DWI-fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can detect stroke that has occurred within 4.5 hours in patients with an unknown time of onset, including patients who awaken with stroke. The introduction of penumbral imaging and MRI-based tissue clocking as imaging biomarkers for stroke has revolutionized stroke therapy, potentially allowing for personalized treatment of eligible stroke patients.

A 3D-Printed Ceramics Innovative Firing Technique: A Numerical and Experimental Study.

Additive manufacturing (AM), also known as three-dimensional (3D) printing, allows the fabrication of complex parts, which are impossible or very expensive to produce using traditional processes. That is the case for dinnerware and artworks (stoneware, porcelain and clay-based products). After the piece is formed, the greenware is fired at high temperatures so that these pieces gain its mechanical strength and aesthetics. The conventional (gas or resistive heating elements) firing usually requires long heating cycles, presently requiring around 10 h to reach temperatures as high as 1200 °C. Searching for faster processes, 3D-printed stoneware were fired using microwave (MW) radiation. The pieces were fired within 10% of the conventional processing time. The temperature were controlled using a pyrometer and monitored using Process Temperature Control Rings (PTCRs). An error of 1.25% was calculated between the PTCR (1207 ± 15 °C) and the pyrometer (1200 °C). Microwave-fast-fired pieces show similar mechanical strength to the references and to the electrically fast-fired pieces (41, 46 and 34 (N/mm2), respectively), presenting aesthetic features closer to the reference. Total porosities of ~4%, ~5% and ~9% were determined for microwave, electrically fast-fired and reference samples. Numerical studies have shown to be essential to better understand and improve the firing process using microwave radiation. In summary, microwave heating can be employed as an alternative to stoneware conventional firing methods, not compromising the quality and features of the processed pieces, and with gains in the heating time.

The BioCascade-VIVAS system for collection and delivery of virus-laden size-fractionated airborne particles

The size of virus-laden particles determines whether aerosol or droplet transmission is dominant in the airborne transmission of pathogens. Determining dominant transmission pathways is critical to implementing effective exposure risk mitigation strategies. The aerobiology discipline greatly needs an air sampling system that can collect virus-laden airborne particles, separate them by particle diameter, and deliver them directly onto host cells without inactivating virus or killing cells. We report the use of a testing system that combines a BioAerosol Nebulizing Generator (BANG) to aerosolize Human coronavirus (HCoV)-OC43 (OC43) and an integrated air sampling system comprised of a BioCascade impactor (BC) and Viable Virus Aerosol Sampler (VIVAS), together referred to as BC-VIVAS, to deliver the aerosolized virus directly onto Vero E6 cells. Particles were collected into four stages according to their aerodynamic diameter (Stage 1: >9.43 μm, Stage 2: 3.81–9.43 μm, Stage 3: 1.41–3.81 μm and Stage 4: <1.41 μm). OC43 was detected by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) analyses of samples from all BC-VIVAS stages. The calculated OC43 genome equivalent counts per cm3 of air ranged from 0.34 ± 0.09 to 70.28 ± 12.56, with the highest concentrations in stage 3 (1.41–3.81 μm) and stage 4 (<1.41 μm). Virus-induced cytopathic effects appeared only in cells exposed to particles collected in stages 3 and 4, demonstrating the presence of viable OC43 in particles <3.81 μm. This study demonstrates the dual utility of the BC-VIVAS as particle size-fractionating air sampler and a direct exposure system for aerosolized viruses. Such utility may help minimize conventional post-collection sample processing time required to assess the viability of airborne viruses and increase the understanding about transmission pathways for airborne pathogens.

On the update frequency of univariate forecasting models

In univariate time series forecasting, models are typically updated at every single review period. This practice, which includes specifying the optimal form of the model and estimating its parameters, theoretically allows the models to exploit new information and to respond quickly to possible structural breaks. We argue that such updates may be irrelevant in practice, also unnecessarily increasing computational cost and forecast instability. Using two large data sets of monthly and daily series as well as an indicative family of conventional time series models, we investigate several model updating scenarios, ranging from complete model form specification and parameter estimation at every review period to no updating at all. We find that intermediate updating scenarios, including the re-estimation of specific parameters but not necessarily the specification of the model form, can result in similar or even better accuracy with significantly lower computational cost. We also show that similar conclusions hold true for popular machine learning methods, as well as for setups where different approaches are utilized for training the models or accelerating their specification and estimation. We discuss the implications of our findings for manufacturers, suppliers, and retailers and propose avenues for future advances in the area of model frequency updating.

Waveform Designs for Joint Wireless Communication and Radar Sensing: Pitfalls and Opportunities

Special attention has been devoted to joint wireless communication and radar sensing concepts over the past decades. Considerable confusion arose, however, with regard to waveform designs. In this paper, various joint wireless communication and radar sensing waveform schemes are reviewed and classified into two categories. Disadvantages and limitations of each category are clarified theoretically. It is shown that waveforms designed merely in conventional time and frequency domains cannot fulfill the conflict requirements posed by radar and wireless communication. As a solution to this fundamental challenge, the multidimensional waveform concepts, along with a generalized correlation model, are proposed for joint wireless communication and radar sensing. To showcase our current research advances in this emerging area, several concrete waveforms designed in multiple dimensions are introduced. Theoretical analysis is validated by simulations and outfield demonstration experiments. It is shown that the suggested waveforms can simultaneously achieve superior detection and communication performances with shared spectrum resources.

Attentive Siamese Networks for Automatic Modulation Classification Based on Multitiming Constellation Diagrams.

Automatic modulation classification (AMC) is an essential part in a cognitive radio receiver. Benefited from the discriminative constellation characteristics among most modulations, AMC methods based on constellation diagrams usually achieve pleasant performance. However, in noncooperation communication systems, constellation diagrams expressing modulations explicitly are difficult to obtain via blind symbol timing synchronization, especially in complicated wireless channels. Therefore, this article proposes a novel constellation diagram-based AMC architecture called attentive Siamese networks (ASNs) by considering multitiming constellation diagrams (MCDs) and selecting the proper symbol timings at the feature level, which is a more robust way than the conventional signal-level symbol timing synchronization. In detail, convolutional neural networks sharing the same parameters first extract deep feature vectors for MCDs. Then, an attention inference module weights all the deep feature vectors. Finally, AMC is realized based on the weighted feature vectors. Moreover, the ASN architecture can be trained end-to-end. Comparing with the state-of-the-art methods that take diverse representations of received baseband signals as input, experimental results based on the RadioML 2018.01A dataset and non-Gaussian noise dataset demonstrate that ASN achieves a remarkable improvement, whose classification accuracy goes over 99% when the signal-to-noise ratio (SNR) > 10 dB.

A study on the interaction among hull, engine and propeller during self-propulsion of a ship

Hull-engine-propeller matching is one of the key issues of ship design. It plays an important role in improving the economy and safety of ship navigation. The hull-engine-propeller matching is generally based on the Factory Acceptance Test of engine and the Sea Acceptance Test of ship. These conventional methods cost money and time. To overcome these defects, this paper proposes a simulation method based on the quasi-steady model of engine and CFD for ship hydrodynamics to study the interaction among the hull, engine and propeller, which provides the basis for ship-engine-propeller matching. Taking the PA6 diesel engine and the ONR tumblehome ship as the study objects, the quasi-steady model of engine is constructed by using simulation software and applied to carry out simulation under the operation conditions; the ship resistance test, propeller open-water test and self-propulsion test are simulated by using CFD method. Based on the simulation results, an integrated simulation platform is established for studying the hull-engine-propeller interaction. Further, the external characteristics of diesel engine under the effects of hull-engine-propeller interaction are predicted for the ship sailing straight ahead at different speeds. This paper provides a new method for studying the characteristics of hull-engine-propeller interaction.

INTRAORAL SCANNING AND CONVENTIONAL IMPRESSION MAY HAVE SIMILAR TIME EFFICIENCY FOR COMPLETE COVERAGE CROWNS AND THREE-UNIT FIXED TOOTH-SUPPORTED PROSTHESES.

Bandiaky ON, Le Bars P, Gaudin A, Hardouin JB, Cheraud-Carpentier M, Mbodj EB, Soueidan A. Comparative assessment of complete-coverage, fixed tooth-supported prostheses fabricated from digital scans or conventional impressions: A systematic review and meta-analysis. J Prosthet Dent. 2022 Jan;127(1):71-79. doi: 10.1016/j.prosdent.2020.09.017. Epub 2020 Nov 2. PMID: 33,143,901. Information not available TYPE OF STUDY/DESIGN: Systematic review with meta-analysis of data.

Identifiability Analysis and Noninvasive Online Estimation of the First-Order Neural Activation Dynamics in the Brain With Closed-Loop Transcranial Magnetic Stimulation.

Neurons demonstrate very distinct nonlinear activation dynamics, influenced by the neuron type, morphology, ion channel expression, and various other factors. The measurement of the activation dynamics can identify the neural target of stimulation and detect deviations, e.g., for diagnosis. This paper describes a tool for closed-loop sequential parameter estimation (SPE) of the activation dynamics through transcranial magnetic stimulation (TMS). The proposed SPE method operates in real time, selects ideal stimulus parameters, detects and processes the response, and concurrently estimates the input-output (IO) curve and the first-order approximation of the activated neural target. To develop a method for concurrent SPE of the first-order activation dynamics and IO curve with closed-loop TMS. First, identifiability of an integrated model of the first-order neural activation dynamics and IO curve is assessed, demonstrating that at least two IO curves need to be acquired with different pulse widths. Then, a two-stage SPE method is proposed. It estimates the IO curve by using Fisher information matrix (FIM) optimization in the first stage and subsequently estimates the membrane time constant as well as the coupling gain in the second stage. The procedure continues in a sequential manner until a stopping rule is satisfied. The results of 73 simulation cases confirm the satisfactory estimation of the membrane time constant and coupling gain with average absolute relative errors (AREs) of 6.2% and 5.3%, respectively, with an average of 344 pulses (172 pulses for each IO curve or pulse width). The method estimates the IO curves' lower and upper plateaus, mid-point, and slope with average AREs of 0.2%, 0.7%, 0.9%, and 14.5%, respectively. The conventional time constant estimation method based on the strength-duration (S-D) curve leads to 33.3% ARE, which is 27.0% larger than 6.2% ARE obtained through the proposed real-time FIM-based SPE method in this paper. SPE of the activation dynamics requires acquiring at least two IO curves with different pulse widths, which needs a controllable TMS (cTMS) device with adjustable pulse duration. The proposed SPE method enhances the cTMS functionality, which can contribute novel insights in research and clinical studies.

Investigating Neurologic Improvement After IV Thrombolysis: The Effect of Time From Stroke Onset vs Imaging-Based Tissue Clock.

Time from stroke onset is associated with clinical response to intravenous thrombolysis (IVT) with alteplase and is therefore used to select patients for treatment. Alternatively, neuroimaging may be used for treatment in the uncertain or extended time window. We hypothesized that the patient-specific imaging indicator of ischemic lesion progression ("tissue clock") using CT perfusion (CTP) or quantitative net water uptake (NWU) is a predictor of early neurologic improvement (ENI) independent of time. Observational study of anterior circulation ischemic stroke patients with proximal vessel occlusion and known time from symptom onset triaged by multimodal CT undergoing endovascular treatment. Quantitative NWU using an established threshold (11.5%) or CTP lesion core mismatch (EXTEND criteria) was used to estimate ischemic lesion progression. The treatment effect of IVT depending on lesion progression defined by tissue clock vs time clock was assessed by inverse probability weighting (IPW). End points were binarized ENI and functional independence at day 90. Four hundred nine patients were included, of which 223 (54.5%) received IVT. The proportion of patients within an early time window (<4.5hours), low NWU, and CTP mismatch were 45.0%, 86.5%, and 80.3%. In IPW, IVT was associated with higher rates of ENI (%-difference: 7.3%, p = 0.02). For patients with CTP mismatch or low NWU, IVT was associated with a 9.6% or 7.2% higher rate of ENI, which was different than the effect of IVT in patients without CTP mismatch or high NWU (-9.3%/-7.3%; p = 0.004/p = 0.03), whereas early treatment window did not modify the effect of IVT. CT-based measures of the "tissue clock" might identify patients who benefit from IVT more accurately than conventional time windows. Considering the high number of patients with early "tissue clock" (low NWU/CTP mismatch) within an extended time window, considerable benefit from IVT using imaging indicators of the "tissue clock" may be achieved.