Fast-ignition design transport studies: realistic electron source, integrated PIC-hydrodynamics, imposed magnetic fields

Transport modeling of idealized, cone-guided fast ignition targets indicates the severe challenge posed by fast-electron source divergence. The hybrid particle-in-cell [PIC] code Zuma is run in tandem with the radiation-hydrodynamics code Hydra to model fast-electron propagation, fuel heating, and thermonuclear burn. The fast electron source is based on a 3D explicit-PIC laser-plasma simulation with the PSC code. This shows a quasi two-temperature energy spectrum, and a divergent angle spectrum (average velocity-space polar angle of 52 degrees). Transport simulations with the PIC-based divergence do not ignite for > 1 MJ of fast-electron energy, for a modest 70 micron standoff distance from fast-electron injection to the dense fuel. However, artificially collimating the source gives an ignition energy of 132 kJ. To mitigate the divergence, we consider imposed axial magnetic fields. Uniform fields ~50 MG are sufficient to recover the artificially collimated ignition energy. Experiments at the Omega laser facility have generated fields of this magnitude by imploding a capsule in seed fields of 50-100 kG. Such imploded fields are however more compressed in the transport region than in the laser absorption region. When fast electrons encounter increasing field strength, magnetic mirroring can reflect a substantial fraction of them and reduce coupling to the fuel. A hollow magnetic pipe, which peaks at a finite radius, is presented as one field configuration which circumvents mirroring.Comment: 16 pages, 17 figures, submitted to Phys. Plasma

Automated information extraction from free-text EEG reports

In this study we have developed a supervised learning to automatically detect with high accuracy EEG reports that describe seizures and epileptiform discharges. We manually labeled 3,277 documents as describing one or more seizures vs no seizures, and as describing epileptiform discharges vs no epileptiform discharges. We then used Naïve Bayes to develop a system able to automatically classify EEG reports into these categories. Our system consisted of normalization techniques, extraction of key sentences, and automated feature selection using cross validation. As candidate features we used key words and special word patterns called elastic word sequences (EWS). Final feature selection was accomplished via sequential backward selection. We used cross validation to predict out of sample performance. Our automated feature selection procedure resulted in a classifier with 38 features for seizure detection, and 23 features for epileptiform discharge detection. The average [95% CI] area under the receiver operating curve was 99.05 [98.79, 99.32]% for detecting reports with seizures, and 96.15 [92.31, 100.00]% for detecting reports with epileptiform discharges. The methodology described herein greatly reduces the manual labor involved in identifying large cohorts of patients for retrospective neurophysiological studies of patients with epilepsy

Variability in pharmacologically-induced coma for treatment of refractory status epilepticus

Objective To characterize the amount of EEG suppression achieved in refractory status epilepticus (RSE) patients treated with pharmacologically-induced coma (PIC). Methods We analyzed EEG recordings from 35 RSE patients between 21–84 years-old who received PIC that target burst suppression and quantified the amount of EEG suppression using the burst suppression probability (BSP). Then we measured the variability of BSPs with respect to a reference level of BSP 0.8 ± 0.15. Finally, we also measured the variability of BSPs with respect to the amount of intravenous anesthetic drugs (IVADs) received by the patients. Results Patients remained in the reference BSP range for only 8% (median, interquartile range IQR [0, 29] %) of the total time under treatment. The median time with BSP below the reference range was 84% (IQR [37, 100] %). BSPs in some patients drifted significantly over time despite constant infusion rates of IVADs. Similar weight-normalized infusion rates of IVADs in different patients nearly always resulted in distinct BSPs (probability 0.93 (IQR [0.82, 1.0]). Conclusion This study quantitatively identified high variability in the amount of EEG suppression achieved in clinical practice when treating RSE patients. While some of this variability may arise from clinicians purposefully deviating from clinical practice guidelines, our results show that the high variability also arises in part from significant inter- and intra- individual pharmacokinetic/pharmacodynamic variation. Our results indicate that the delicate balance between maintaining sufficient EEG suppression in RSE patients and minimizing IVAD exposure in clinical practice is challenging to achieve. This may affect patient outcomes and confound studies seeking to determine an optimal amount of EEG suppression for treatment of RSE. Therefore, our analysis points to the need for developing an alternative paradigm, such as vigilant anesthetic management as happens in operating rooms, or closed-loop anesthesia delivery, for investigating and providing induced-coma therapy to RSE patients

Rapid In-Vitro Inactivation of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Using Povidone-Iodine Oral Antiseptic Rinse

Purpose To investigate the optimal contact time and concentration for viricidal activity of oral preparation of povidone‐iodine (PVP‐I) against SARS‐CoV‐2 (‘corona virus’) to mitigate the risk and transmission of the virus in the dental practice. Materials and Methods The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) USA‐WA1/2020 strain, virus stock was tested against oral antiseptic solutions consisting of aqueous povidone‐iodine (PVP‐I) as the sole active ingredient. The PVP‐I was tested at diluted concentrations of 0.5%, 1%, and 1.5%. Test media without any virus was added to 2 tubes of the compounds to serve as toxicity and neutralization controls. Ethanol (70%) was tested in parallel as a positive control, and water only as a negative control. The test solutions and virus were incubated at room temperature (22 ± 2 °C) for time periods of 15 and 30 seconds. The solution was then neutralized by a 1/10 dilution in minimum essential medium (MEM) 2% fetal bovine serum (FBS), 50 µg/mL gentamicin. Surviving virus from each sample was quantified by standard end‐point dilution assay and the log reduction value (LRV) of each compound compared to the negative (water) control was calculated. Results PVP‐I oral antiseptics at all tested concentrations of 0.5%, 1%, and 1.5%, completely inactivated SARS‐CoV‐2 within 15 seconds of contact. The 70% ethanol control group was unable to completely inactivate SARS‐CoV‐2 after 15 seconds of contact, but was able to inactivate the virus at 30 seconds of contact. Conclusions PVP‐I oral antiseptic preparations rapidly inactivated SARS‐CoV‐2 virus in vitro. The viricidal activity was present at the lowest concentration of 0.5 % PVP‐I and at the lowest contact time of 15 seconds. This important finding can justify the use of preprocedural oral rinsing with PVP‐I (for patients and health care providers) may be useful as an adjunct to personal protective equipment, for dental and surgical specialties during the COVID‐19 pandemic

Comparison of In Vitro Inactivation of SARS CoV-2 with Hydrogen Peroxide and Povidone-Iodine Oral Antiseptic Rinses

Purpose To evaluate the in vitro inactivation of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) with hydrogen peroxide (H2O2) and povidone‐iodine (PVP‐I) oral antiseptic rinses at clinically recommended concentrations and contact times. Materials and Methods SARS‐CoV‐2, USA‐WA1/2020 strain virus stock was prepared prior to testing by growing in Vero 76 cells. The culture media for prepared virus stock was minimum essential medium (MEM) with 2% fetal bovine serum (FBS) and 50 µg/mL gentamicin. Test compounds consisting of PVP‐I oral rinse solutions and H2O2 aqueous solutions were mixed directly with the virus solution so that the final concentration was 50% of the test compound and 50% of the virus solution. Thus PVP‐I was tested at concentrations of 0.5%, 1.25%, and 1.5%, and H2O2 was tested at 3% and 1.5% concentrations to represent clinically recommended concentrations. Ethanol and water were evaluated in parallel as standard positive and negative controls. All samples were tested at contact periods of 15 seconds and 30 seconds. Surviving virus from each sample was then quantified by standard end‐point dilution assay and the log reduction value of each compound compared to the negative control was calculated. Results After the 15‐second and 30‐second contact times, PVP‐I oral antiseptic rinse at all 3 concentrations of 0.5%, 1.25%, and 1.5% completely inactivated SARS‐CoV‐2. The H2O2 solutions at concentrations of 1.5% and 3.0% showed minimal viricidal activity after 15 seconds and 30 seconds of contact time. Conclusions SARS‐CoV‐2 virus was completely inactivated by PVP‐I oral antiseptic rinse in vitro, at the lowest concentration of 0.5 % and at the lowest contact time of 15 seconds. Hydrogen peroxide at the recommended oral rinse concentrations of 1.5% and 3.0% was minimally effective as a viricidal agent after contact times as long as 30 seconds. Therefore, preprocedural rinsing with diluted PVP‐I in the range of 0.5% to 1.5% may be preferred over hydrogen peroxide during the COVID‐19 pandemic

An enhanced cerebral recovery index for coma prognostication following cardiac arrest

Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. In this paper we describe a novel retrospective data set of 167 cardiac arrest patients (spanning three institutions) who received electroencephalography (EEG) monitoring. We utilized a subset of the collected data to generate features that measured the connectivity, complexity and category of EEG activity. A subset of these features was included in a logistic regression model to estimate a dichotomized cerebral performance category score at discharge. We compared the predictive performance of our method against an established EEG-based alternative, the Cerebral Recovery Index (CRI) and show that our approach more reliably classifies patient outcomes, with an average increase in AUC of 0.27

Automated tracking of level of consciousness and delirium in critical illness using deep learning

Over- and under-sedation are common in the ICU, and contribute to poor ICU outcomes including delirium. Behavioral assessments, such as Richmond Agitation-Sedation Scale (RASS) for monitoring levels of sedation and Confusion Assessment Method for the ICU (CAM-ICU) for detecting signs of delirium, are often used. As an alternative, brain monitoring with electroencephalography (EEG) has been proposed in the operating room, but is challenging to implement in ICU due to the differences between critical illness and elective surgery, as well as the duration of sedation. Here we present a deep learning model based on a combination of convolutional and recurrent neural networks that automatically tracks both the level of consciousness and delirium using frontal EEG signals in the ICU. For level of consciousness, the system achieves a median accuracy of 70% when allowing prediction to be within one RASS level difference across all patients, which is comparable or higher than the median technician-nurse agreement at 59%. For delirium, the system achieves an AUC of 0.80 with 69% sensitivity and 83% specificity at the optimal operating point. The results show it is feasible to continuously track level of consciousness and delirium in the ICU

Planets in Stellar Clusters Extensive Search. II. Discovery of 57 Variables in the Cluster NGC 2158 with Millimagnitude Image Subtraction Photometry

We have undertaken a long-term project, Planets in Stellar Clusters Extensive Search (PISCES), to search for transiting planets in open clusters. NGC 2158 is one of the targets we have chosen -- an intermediate age, populous, rather metal poor cluster. In this paper we present the results of a search for variable stars in the data from the first season of monitoring at the FLWO 1.2 m telescope. This is the first variability search ever conducted in this cluster. We present a catalog of 57 variable stars, most with low amplitude variability. Among the variables is a cataclysmic variable (CV) which underwent a 2.5 mag outburst. If it is a member of NGC 2158, this would be the fourth CV known in an open cluster. We have also found five delta Scuti stars, three of which we have two or more detectable modes of pulsation. Of the 57 variables discovered, 28 have R-band amplitudes of 5% or below. Six of those vary at or below the 2% level, including one with 0.08% variability.Comment: 13 pages LaTeX, including 8 figures and 4 tables, submitted to Astronomical Journal. Version with full resolution figures available through ftp at ftp://cfa-ftp.harvard.edu/pub/bmochejs/PISCES/papers/2_N2158

Characterization of a Novel STAT 2 Knock Out Hamster Model of Crimean Congo Hemorrhagic Fever Virus Pathogenesis

Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne pathogen causing a febrile illness in humans, which can progress to hemorrhagic manifestations, multi-organ failure, and death. Current mouse models of CCHFV infection reliably succumb to virus challenge but vary in their ability to reflect signs of disease similar to humans. In this study, we established a signal transducer and activator of transcription 2 (STAT2) knockout hamster model to expand the repertoire of animal models of CCHFV pathogenesis that can be used for therapeutic development. These hamsters demonstrated a systemic and lethal disease in response to infection. Hallmarks of human disease were observed including petechial rash, blood coagulation dysfunction, and various biochemistry and blood cell count abnormalities. Furthermore, we also demonstrated the utility of this model for anti-CCHFV therapeutic evaluation. The STAT2 knock-out hamster model of CCHFV infection may provide some further insights into clinical disease, viral pathogenesis, and pave the way for testing of potential drug and vaccine candidates