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OBJECTIVES:. Extracorporeal membrane oxygenator support is a powerful clinical tool that is currently enjoying a resurgence in popularity. Wider use of extracorporeal membrane oxygenator support is limited by its significant risk profile and extreme consumption of resources. This study examines the role of markers of liver dysfunction in predicting outcomes of adult patients requiring extracorporeal membrane oxygenator support. DESIGN:. Retrospective review. SETTING:. Large extracorporeal membrane oxygenator center, Chicago, IL. PATIENTS:. This study reports a single institution experience examining all adult patients for whom extracorporeal membrane oxygenator support was used over an 8-year period. Data were collected regarding patient demographics, details of extracorporeal membrane oxygenator support provided, laboratory data, and outcomes. Trends in liver function were examined for their ability to predict survival. INTERVENTION:. Extracorporeal membrane oxygenator support, critical care. MEASUREMENTS AND MAIN RESULTS:. Mean age was 50 years (range, 19–82 yr). There were 86 male patients (56.6%) and 66 female patients (43.4%). Indications for initiation of extracorporeal membrane oxygenator support included cardiac 76 patients (50.0%), respiratory 48 patients (31.6%), extracorporeal cardiopulmonary resuscitation 21 patients (13.3%), and combined cardiac/respiratory seven patients (4.6%). Mean duration of extracorporeal membrane oxygenator support was 17 days (range 1–223 d) or median 8 days (interquartile range, 4–17 d). Overall, in-hospital mortality was 56% (86/152). Forty-five percent of adult patients (68/152) surpassed at least one of the following established liver dysfunction thresholds: total bilirubin greater than 15 mg/dL, aspartate aminotransferase greater than 20× upper limit of normal, and alanine aminotransferase greater than 20× upper limit of normal. The multivariable logistic analysis yielded three significant findings associated with in-hospital mortality: highest total bilirubin greater than 15 (adjusted odds ratio = 4.40; 95% CI, 1.19–21.87; p = 0.04), age (adjusted odds ratio = 1.03; 95% CI, 1.00–1.05; p = 0.04), and highest lactate (adjusted odds ratio = 1.15; 95% CI, 1.06–1.26; p = 0.002). CONCLUSIONS:. Increases in age, highest total bilirubin, and lactate all correlated with in-hospital mortality in multivariable analysis of patients requiring extracorporeal membrane oxygenator support.

A new optical diagnostic method that predicts the global fuel-air equivalence ratio of a swirl combustor using absorption spectra from only three optical paths is proposed here. Under normal operation, the global equivalence ratio and total flow rate determine the temperature and concentration fields of the combustor, which subsequently determine the absorption spectra of any combustion species. Therefore, spectra, as the fingerprint for a produced combustion field, were employed to predict the global equivalence ratio, one of the key operational parameters, in this study. Specifically, absorption spectra of water vapor at wavenumbers around 7444.36, 7185.6, and 6805.6 cm -1 measured at three different downstream locations of the combustor were used to predict the global equivalence ratio. As it is difficult to find analytical relationships between the spectra and produced combustion fields, a predictive model was a data-driven acquisition. The absorption spectra as an input were first feature-extracted through stacked convolutional autoencoders and then a dense neural network was used for regression prediction between the feature scores and the global equivalence ratio. The model could predict the equivalence ratio with an absolute error of ±0.025 with a probability of 96%, and a gradient-weighted regression activation mapping analysis revealed that the model leverages not only the peak intensities but also the variations in the shape of absorption lines for its predictions., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ultraviolet-C (UV-C) radiation and ozone gas are potential mechanisms employed to inactivate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), each exhibiting distinct molecular-level modalities of action. To elucidate these disparities and deepen our understanding, we delve into the intricacies of SARS-CoV-2 inactivation via UV-C and ozone gas treatments, exploring their distinct molecular-level impacts utilizing a suite of advanced techniques, including biological atomic force microscopy (Bio-AFM) and single virus force spectroscopy (SVFS). Whereas UV-C exhibited no perceivable alterations in virus size or surface topography, ozone gas treatment elucidated pronounced changes in both parameters, intensifying with prolonged exposure. Furthermore, a nuanced difference was observed in virus-host cell binding post-treatment: ozone gas distinctly reduced SARS-CoV-2 binding to host cells, while UV-C maintained the status quo. The results derived from these methodical explorations underscore the pivotal role of advanced Bio-AFM techniques and SVFS in enhancing our understanding of virus inactivation mechanisms, offering invaluable insights for future research and applications in viral contamination mitigation.

Introduction: Pain that arises spontaneously is considered more clinically relevant than pain evoked by external stimuli. However, measuring spontaneous pain in animal models in preclinical studies is challenging due to methodological limitations. To address this issue, recently we developed a deep learning (DL) model to assess spontaneous pain using cellular calcium signals of the primary somatosensory cortex (S1) in awake head-fixed mice. However, DL operate like a "black box", where their decision-making process is not transparent and is difficult to understand, which is especially evident when our DL model classifies different states of pain based on cellular calcium signals. In this study, we introduce a novel machine learning (ML) model that utilizes features that were manually extracted from S1 calcium signals, including the dynamic changes in calcium levels and the cell-to-cell activity correlations., Method: We focused on observing neural activity patterns in the primary somatosensory cortex (S1) of mice using two-photon calcium imaging after injecting a calcium indicator (GCaMP6s) into the S1 cortex neurons. We extracted features related to the ratio of up and down-regulated cells in calcium activity and the correlation level of activity between cells as input data for the ML model. The ML model was validated using a Leave-One-Subject-Out Cross-Validation approach to distinguish between non-pain, pain, and drug-induced analgesic states., Results and Discussion: The ML model was designed to classify data into three distinct categories: non-pain, pain, and drug-induced analgesic states. Its versatility was demonstrated by successfully classifying different states across various pain models, including inflammatory and neuropathic pain, as well as confirming its utility in identifying the analgesic effects of drugs like ketoprofen, morphine, and the efficacy of magnolin, a candidate analgesic compound. In conclusion, our ML model surpasses the limitations of previous DL approaches by leveraging manually extracted features. This not only clarifies the decision-making process of the ML model but also yields insights into neuronal activity patterns associated with pain, facilitating preclinical studies of analgesics with higher potential for clinical translation., Competing Interests: MSB, HP, and HY are employed by Neurogrin Inc., which was founded by GC, SJK and SKK. Neurogrin Inc. holds the patent application related to the contents of this article (10-2023-0025640 in Korea). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Bak, Park, Yoon, Chung, Shin, Shin, Kim, Lee, Nägerl, Kim and Kim.)

Two forms of plasticity, synaptic and intrinsic, are neural substrates for learning and memory. Abnormalities in homeostatic plasticity cause severe neuropsychiatric diseases, such as schizophrenia and autism. This suggests that the balance between synaptic transmission and intrinsic excitability is important for physiological function in the brain. Despite the established role of synaptic plasticity between parallel fiber (PF) and Purkinje cell (PC) in fear memory, its relationship with intrinsic plasticity is not well understood. Here, patch clamp recording revealed depression of intrinsic excitability in PC following auditory fear conditioning (AFC). Depressed excitability balanced long-term potentiation of PF-PC synapse to serve homeostatic regulation of PF-evoked PC firing. We then optogenetically manipulated PC excitability during the early consolidation period resulting in bidirectional regulation of fear memory. Fear conditioning-induced synaptic plasticity was also regulated following optogenetic manipulation. These results propose intrinsic plasticity in PC as a novel mechanism of fear memory and elucidate that decreased intrinsic excitability in PC counterbalances PF-PC synaptic potentiation to maintain fear memory in a normal range., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

We propose a novel technique to accurately predict carbon dioxide (CO 2 ) concentrations even in flow fields with temperature gradients based on a single laser path absorption spectrum measurement and machine learning. Concentration measurements in typical tunable diode laser absorption spectroscopy are based on a ratio of two integrated absorbances, each from a spectral line with different temperature dependence. However, the inferred concentrations can deviate significantly from the actual concentrations in the presence of temperature gradients. Furthermore, it is also difficult to find an analytical expression to compensate for the effect of nonuniform temperature profiles on concentration measurements. In this study, the entire absorption feature was considered since its shape and peak intensities vary with temperature and concentration. Specifically, a predictive model is obtained in a data-driven manner that can identify and compensate for the effect of a nonuniform temperature field on the spectrum. Despite a very detailed understanding of the CO 2 absorption spectrum, it is nearly impossible to collect sufficient spectra for model acquisition by varying all temperature gradient conditions. Therefore, the model was obtained using only simulated data, much like the concept of a "digital twin". Finally, the predictive performance of the acquired model was verified using experimental data. In all test cases, the predictive performance of the model was superior to that of the two-line method. Additionally, a gradient-weighted regression activation mapping analysis confirmed that the model utilizes both the peak intensities as well as the change in the shape of absorption lines for prediction., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Quantification of tyrosine hydroxylase (TH)-positive neurons is essential for the preclinical study of Parkinson's disease (PD). However, manual analysis of immunohistochemical (IHC) images is labor-intensive and has less reproducibility due to the lack of objectivity. Therefore, several automated methods of IHC image analysis have been proposed, although they have limitations of low accuracy and difficulties in practical use. Here, we developed a convolutional neural network-based machine learning algorithm for TH+ cell counting. The developed analytical tool showed higher accuracy than the conventional methods and could be used under diverse experimental conditions of image staining intensity, brightness, and contrast. Our automated cell detection algorithm is available for free and has an intelligible graphical user interface for cell counting to assist practical applications. Overall, we expect that the proposed TH+ cell counting tool will promote preclinical PD research by saving time and enabling objective analysis of IHC images.

A method, which can accurately measure carbon emission and gas temperature simultaneously in real-time from a laser-induced breakdown spectrum (LIBS) via machine learning, is proposed in this study. In typical, peak intensity ratios had been used to map species concentrations prior to plasma formation, after removing the broadband continuum of the spectrum; however, the dependence of these peak intensity ratios on the concentration changes with the change in gas density. Therefore, considering the fact that the strength and shape of this broadband continuum is a function of the gas density for a given optical setup, we attempted to collect a spectrum by shortening the time delay after the laser fire, such that the spectrum can contain some of the broadband continuum. Since the analytical quantification of this broadband continuum is not trivial, we employed a machine learning approach to acquire a model that simultaneously predicts the gas temperature and CO 2 concentration. The predictive performance of the model trained with spectra that contain the broadband continuum was much better than that without it; the gradient-weighted regression activation mapping (Grad-RAM) analysis revealed that the model utilizes the broadband spectrum for temperature prediction and correction of changes in peak intensity due to temperature changes in the concentration prediction process.

Itch and pain are distinct sensations that share anatomically similar pathways: from the periphery to the brain. Over the last decades, several itch-specific neural pathways and molecular markers have been identified at the peripheral and spinal cord levels. Although the perception of sensation is ultimately generated at the brain level, how the brain separately processes the signals is unclear. The primary somatosensory cortex (S1) plays a crucial role in the perception of somatosensory information, including touch, itch, and pain. In this study, we investigated how S1 neurons represent itch and pain differently. First, we established a spontaneous itch and pain mouse model. Spontaneous itch or pain was induced by intradermal treatment with 5-HT or capsaicin on the lateral neck and confirmed by a selective increase in scratching or wiping-like behavior, respectively. Next, in vivo two-photon calcium imaging was performed in awake mice after four different treatments, including 5-HT, capsaicin, and each vehicle. By comparing the calcium activity acquired during different sessions, we distinguished the cells responsive to itch or pain sensations. Of the total responsive cells, 11% were both responsive, and their activity in the pain session was slightly higher than that in the itch session. Itch- and painpreferred cells accounted for 28.4% and 60.6%, respectively, and the preferred cells showed the lowest activity in their counter sessions. Therefore, our results suggest that S1 uses a multiplexed coding strategy to encode itch and pain, and S1 neurons represent the interaction between itch and pain.

The contaminated healthcare environment plays an important role in the spread of multidrug-resistant organisms (MDROs) and Clostridioides difficile. This study aimed to evaluate the antimicrobial effects of ozone generated by a dielectric barrier discharge (DBD) plasma reactor on various materials that were contaminated by vancomycin-resistant Enterococcus faecium (VRE), carbapenem-resistant Klebsiella pneumoniae (CRE), carbapenem-resistant Pseudomonas aeruginosa (CRPA), carbapenem-resistant Acinetobacter baumannii (CRAB) and C. difficile spores. Various materials contaminated by VRE, CRE, CRPA, CRAB and C. difficile spores were treated with different ozone concentrations and exposure times. Atomic force microscopy (AFM) demonstrated bacterial surface modifications following ozone treatment. When an ozone dosage of 500 ppm for 15 min was applied to VRE and CRAB, about 2 or more log 10 reduction was observed in stainless steel, fabric and wood, and a 1-2 log 10 reduction in glass and plastic. Spores of C. difficile were more resistant to ozone than were all other tested organisms. On AFM, the bacterial cells, following ozone treatment, were swollen and distorted. The ozone generated by the DBD plasma reactor provided a simple and valuable decontamination tool for the MDROs and C. difficile spores, which are known as common pathogens in healthcare-associated infections., (© 2022. The Author(s).)

Chronic pain remains an intractable condition in millions of patients worldwide. Spontaneous ongoing pain is a major clinical problem of chronic pain and is extremely challenging to diagnose and treat compared to stimulus-evoked pain. Although extensive efforts have been made in preclinical studies, there still exists a mismatch in pain type between the animal model and humans (i.e., evoked vs. spontaneous), which obstructs the translation of knowledge from preclinical animal models into objective diagnosis and effective new treatments. Here, we developed a deep learning algorithm, designated AI-bRNN (Average training, Individual test-bidirectional Recurrent Neural Network), to detect spontaneous pain information from brain cellular Ca 2+ activity recorded by two-photon microscopy imaging in awake, head-fixed mice. AI-bRNN robustly determines the intensity and time points of spontaneous pain even in chronic pain models and evaluates the efficacy of analgesics in real time. Furthermore, AI-bRNN can be applied to various cell types (neurons and glia), brain areas (cerebral cortex and cerebellum) and forms of somatosensory input (itch and pain), proving its versatile performance. These results suggest that our approach offers a clinically relevant, quantitative, real-time preclinical evaluation platform for pain medicine, thereby accelerating the development of new methods for diagnosing and treating human patients with chronic pain., (© 2022. The Author(s).)

Histone modifications are a key mechanism underlying the epigenetic regulation of gene expression, which is critically involved in the consolidation of multiple forms of memory. However, the roles of histone modifications in cerebellum-dependent motor learning and memory are not well understood. To test whether changes in histone methylation are involved in cerebellar learning, we used heterozygous Kdm3b knockout (Kdm3b +/- ) mice, which show reduced lysine 9 on histone 3 (H3K9) demethylase activity. H3K9 di-methylation is significantly increased selectively in the granule cell layer of the cerebellum of Kdm3b +/- mice. In the cerebellum-dependent optokinetic response (OKR) learning, Kdm3b +/- mice show deficits in memory consolidation, whereas they are normal in basal oculomotor performance and OKR acquisition. In addition, RNA-seq analyses revealed that the expression levels of several plasticity-related genes were altered in the mutant cerebellum. Our study suggests that active regulation of histone methylation is critical for the consolidation of cerebellar motor memory.

In this study, we demonstrate successful development of a predictive model that detects both the fuel-air equivalence ratio (ϕ) and local pressure prior to plasma formation via machine-learning from the laser-induced plasma spectra; the resulting model enables measurement of a wide range of fuel concentrations and pressures. The process of model acquisition is composed of three steps: (i) normalization of the spectra, (ii) feature extraction and selection, and (iii) training of an artificial neural network (ANN) with feature scores and the corresponding labels. In detail, the spectra were first normalized by the total emission intensity; then principal component analysis (PCA) or independent component analysis (ICA) was carried out for feature extraction and selection. Subsequently, the scores of these principal or independent components as inputs were trained for the ANN with expected ϕ and pressure values for outputs, respectively. The model acquisition was successful, and the model's predictive performance was validated by predicting the ϕ and pressure in the test dataset.

Neuropathic pain is an intractable chronic pain, caused by damage to the somatosensory nervous system. To date, treatment for neuropathic pain has limited effects. For the development of efficient therapeutic methods, it is essential to fully understand the pathological mechanisms of neuropathic pain. Besides abnormal sensitization in the periphery and spinal cord, accumulating evidence suggests that neural plasticity in the brain is also critical for the development and maintenance of this pain. Recent technological advances in the measurement and manipulation of neuronal activity allow us to understand maladaptive plastic changes in the brain during neuropathic pain more precisely and modulate brain activity to reverse pain states at the preclinical and clinical levels. In this review paper, we discuss the current understanding of pathological neural plasticity in the four pain-related brain areas: the primary somatosensory cortex, the anterior cingulate cortex, the periaqueductal gray, and the basal ganglia. We also discuss potential treatments for neuropathic pain based on the modulation of neural plasticity in these brain areas.

Tacts facilitate social interaction, and a strong tact repertoire can lead to the development of other verbal operants. For children with autism spectrum disorder (ASD), the development of a tact repertoire can reduce stereotypical and repetitive language and increase social communication, as functional language may reduce the amount of stereotypical vocal behavior that children engage in. However, teaching tact repertoires to children with ASD that maintain and generalize is difficult. The current study reviewed tact interventions for children with ASD from 2000 to 2019 to provide an overview of current tact interventions, their effectiveness, and the inclusion of intervention components that may promote maintenance and generalization of learned tacts in children with ASD. Fifty-one studies were included in the review. Of the studies that met criteria for effect size calculations 87.18% of the interventions showed excellent or high effect. Although many of the studies focused more on stimulus control to answer specific research questions, some studies implemented intervention components and procedures that could promote acquisition and generalization of learned tacts in children with ASD. We discuss implications and the need to increase research regarding tact intervention components that can increase generalization in children with ASD., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

During the COVID-19 pandemic, face masks have become limited in stock. Most of sterilization methods are not applicable for eliminating virus from face masks without compromising the filtration efficiency of the masks. In this study, using a human coronavirus (HCoV-229E) as a surrogate for SARS-CoV-2 contamination on KF94 face masks, we show that the virus loses its infectivity with a 4 log reduction when exposed for 10 s to 120 ppm ozone gas produced by a dielectric barrier discharge plasma generator. Scanning electron microscopy, particulate filtration efficiency (PFE), and inhalation resistance tests revealed that there was no detectable structural or functional deterioration observed in the electrocharged filter layer of Korea Filter (KF) 94 masks even after their excessive exposure to ozone. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) showed decreases in amplification efficiency of HCoV-229E RNA recovered from masks exposed to ozone, indicating the damage to the RNA by the ozone treatment. Our results demonstrate that the plasma generator rapidly disinfects contaminated face masks at least five times without compromising filtration efficiency., Competing Interests: The authors declare no competing financial interest., (© 2021 American Chemical Society.)

A new technique is developed for reconstructing the temperature and species-concentration fields by employing tunable diode laser absorption spectroscopy (TDLAS) and laser-induced breakdown spectroscopy (LIBS) on axisymmetric combustion fields. For two-line thermometry, the uncertainties in linestrengths of the absorption lines may cause systematic errors in temperature and species concentration estimations. Thus, the radial profiles of water vapor concentration are obtained first using the LIBS, assuming that the combustion is complete; then, the radial temperature profiles are estimated from the radial profiles of absorption coefficient, as reconstructed from the absorbance profiles obtained using the TDLAS. The spectral lines of water vapor at 7185.6 and 7444.36 cm -1 are selected as the linestrengths show monotonic decreases with the increase in temperature within the measuring temperature range. The radial profiles of temperature and water mole fraction are well-reconstructed, and the measurement error is found to be as low as 3%. The technique yielded higher temperatures compared to the thermocouple, possibly owing to the significant radiative heat loss in the thermocouple data.

Alzheimer's disease (AD) is a neurodegenerative disorder associated with deficits in cognition and synaptic plasticity. While accumulation of amyloid β (Aβ) and hyper-phosphorylation of tau are parts of the etiology, AD can be caused by a large number of different genetic mutations and other unknown factors. Considering such a heterogeneous nature of AD, it would be desirable to develop treatment strategies that can improve memory irrespective of the individual causes. Reducing the phosphorylation of eukaryotic translation initiation factor 2α (eIF2α) was shown to enhance long-term memory and synaptic plasticity in naïve mice. Moreover, hyper-phosphorylation of eIF2α is observed in the brains of postmortem AD patients. Therefore, regulating eIF2α phosphorylation can be a plausible candidate for restoring memory in AD by targeting memory-enhancing mechanism. In this study, we examined whether PKR inhibition can rescue synaptic and learning deficits in two different AD mouse models; 5XFAD transgenic and Aβ 1-42 -injected mice. We found that the acute treatment of PKR inhibitor (PKRi) can restore the deficits in long-term memory and long-term potentiation (LTP) in both mouse models without affecting the Aβ load in the hippocampus. Our results prove the principle that targeting memory enhancing mechanisms can be a valid candidate for developing AD treatment.

Unlabelled: MicroRNAs (miRNAs) are small, noncoding RNAs that posttranscriptionally regulate gene expression in many tissues. Although a number of brain-enriched miRNAs have been identified, only a few specific miRNAs have been revealed as critical regulators of synaptic plasticity, learning, and memory. miR-9-5p/3p are brain-enriched miRNAs known to regulate development and their changes have been implicated in several neurological disorders, yet their role in mature neurons in mice is largely unknown. Here, we report that inhibition of miR-9-3p, but not miR-9-5p, impaired hippocampal long-term potentiation (LTP) without affecting basal synaptic transmission. Moreover, inhibition of miR-9-3p in the hippocampus resulted in learning and memory deficits. Furthermore, miR-9-3p inhibition increased the expression of the LTP-related genes Dmd and SAP97, the expression levels of which are negatively correlated with LTP. These results suggest that miR-9-3p-mediated gene regulation plays important roles in synaptic plasticity and hippocampus-dependent memory., Significance Statement: Despite the abundant expression of the brain-specific microRNA miR-9-5p/3p in both proliferating and postmitotic neurons, most functional studies have focused on their role in neuronal development. Here, we examined the role of miR-9-5p/3p in adult brain and found that miR-9-3p, but not miR-9-5p, has a critical role in hippocampal synaptic plasticity and memory. Moreover, we identified in vivo binding targets of miR-9-3p that are involved in the regulation of long-term potentiation. Our study provides the very first evidence for the critical role of miR-9-3p in synaptic plasticity and memory in the adult mouse., (Copyright © 2016 the authors 0270-6474/16/368641-12$15.00/0.)

A simulation is developed to investigate the kinetics of nitric oxide (NO) formation in premixed methane/air combustion stabilized by nanosecond-pulsed discharges. The simulation consists of two connected parts. The first part calculates the kinetics within the discharge while considering both plasma/combustion reactions and species diffusion, advection and thermal conduction to the surrounding flow. The second part calculates the kinetics of the overall flow after mixing the discharge flow with the surrounding flow to account for the effect that the discharge has on the overall kinetics. The simulation reveals that the discharge produces a significant amount of atomic oxygen (O) as a result of the high discharge temperature and dissociative quenching of excited state nitrogen by molecular oxygen. This atomic oxygen subsequently produces hydroxyl (OH) radicals. The fractions of these O and OH then undergo Zel'dovich reactions and are found to contribute to as much as 73% of the total NO that is produced. The post-discharge simulation shows that the NO survives within the flow once produced., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)

Laser ablation has been used to study successive ignition in premixed methane/air mixtures under conditions in which the flow speed leads to flame blow-out. A range of laser pulse frequencies is experimentally mimicked by varying the time interval between two closely spaced laser pulses. Emission intensities from the laser ablation kernels are measured to qualitatively estimate laser energy coupling, and flame CH* chemiluminescence is recorded in a time-resolved manner to capture the flame evolution and propagation. A comparison of the measurements is made between the two successive breakdown ignition events. It is found that the formation of the subsequent ablation kernel is almost independent of the previous one, however, for the successive breakdowns, the first breakdown and its ensuing combustion created temporal regions of no energy coupling as they heat the gas and lower the density. Flame imaging shows that the second ablation event successfully produces another flame kernel and is capable of holding the flame-base even at pulse intervals where the second laser pulse cannot form a breakdown. This study demonstrates that successive ablation ignition can allow for the use of higher laser frequencies and enhanced flame stabilization than successive breakdown ignition.

A sensitive radioactive aerosols sensor has been designed and developed. Its design guidance is based on the need for a low operational cost and reliable measurements to provide daily aerosol monitoring. The exposure of diethylene-glycol bis (allylcarbonate) to radiation causes modification of its physico-chemical properties like surface roughness and reflectance. In the present study, optical sensor based on the reflectance measurement has been developed with an aim to monitor real time presence of alpha radioactive aerosols emitted from thorium nitrate hydrate. The results shows that the fabricated sensor can detect 0.0157 kBq to 0.1572 kBq of radio activity by radioactive aerosols generated from (Th(NO3)4 ⋅ 5H2O) at 0.1 ml/min flow rate. The proposed instrument will be helpful to monitor radioactive aerosols in/around a nuclear facility, building construction sites, mines, and granite polishing factories.