Calculation of Transactinide Homolog Isotope Production Reactions Possible with the Center for Accelerator Mass Spectrometry (CAMS) at Lawrence Livermore National Laboratory

The LLNL heavy element group has been investigating the chemical properties of the heaviest elements over the past several years. The properties of the transactinides (elements with Z > 103) are often unknown due to their low production rates and short half-lives, which require lengthy cyclotron irradiations in order to make enough atoms for statistically significant evaluations of their chemistry. In addition, automated chemical methods are often required to perform consistent and rapid chemical separations on the order of minutes for the duration of the experiment, which can last from weeks to months. Separation methods can include extraction chromatography, liquid-liquid extraction, or gas-phase chromatography. Before a lengthy transactinide experiment can be performed at an accelerator, a large amount of preparatory work must be done both to ensure the successful application of the chosen chemical system to the transactinide chemistry problem being addressed, and to evaluate the behavior of the lighter elemental homologs in the same chemical system. Since transactinide chemistry is literally performed on one single atom, its chemical properties cannot be determined from bulk chemical matrices, but instead must be inferred from the behavior of the lighter elements that occur in its chemical group and in those of its neighboring elements. By first studying the lighter group homologs in a particular chemical system, when the same system is applied to the transactinide element under investigation, its decay properties can be directly compared to those of the homologues, thereby allowing an inference of its own chemistry. The Center for Accelerator Mass Spectrometry (CAMS) at Lawrence Livermore National Laboratory (LLNL) includes a 1 MV Tandem accelerator, capable of accelerating light ions such as protons to energies of roughly 15 MeV. By using the CAMS beamline, tracers of transactinide homolog elements can be produced both for development of chemical systems and for evaluation of homolog chemical properties. CAMS also offers an environment for testing these systems 'online' by incorporating automated chemical systems into the beamline so that tracers can be created, transported, and chemically separated all on the shorter timescales required for transactinide experiments. Even though CAMS is limited in the types and energies of ions they can accelerate, there are still a wide variety of reactions that can be performed there with commercially available target materials. The half-lives of these isotopes vary over a range that could be used for both online chemistry (where shorter half-lives are required) and benchtop tracers studies (where longer lived isotopes are preferred). In this document, they present a summary of tracer production reactions that could be performed at CAMS, specifically for online, automated chemical studies. They are from chemical groups four through seven, 13, and 14, which would be appropriate for studies of elements 104-107, 113, and 114. Reactions were selected that had (a) commercially available target material, (b) half-lives long enough for transport from a target chamber to an automated chemistry system, and (c) cross-sections at CAMS available projectile energies that were large enough to produce enough atoms to result in a statistically relevant signal after losses for transport and chemistry were considered. In addition, the resulting product atoms had to decay with an observable gamma-ray using standard Ge gamma-ray detectors. The table includes calculations performed for both metal targets and their corresponding oxides

Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19.

Traveller screening is being used to limit further spread of COVID-19 following its recent emergence, and symptom screening has become a ubiquitous tool in the global response. Previously, we developed a mathematical model to understand factors governing the effectiveness of traveller screening to prevent spread of emerging pathogens (Gostic et al., 2015). Here, we estimate the impact of different screening programs given current knowledge of key COVID-19 life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected people. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. Our work underscores the need for measures to limit transmission by individuals who become ill after being missed by a screening program. These findings can support evidence-based policy to combat the spread of COVID-19, and prospective planning to mitigate future emerging pathogens

Report on 240Am(n,x) surrogate cross section test measurement

The main goal of the test measurement was to determine the feasibility of the {sup 243}Am(p,t) reaction as a surrogate for {sup 240}Am(n,f). No data cross section data exists for neutron induced reactions on {sup 240}Am; the half-life of this isotope is only 2.1 days making direct measurements difficult, if not impossible. The 48-hour experiment was conducted using the STARS/LIBERACE experimental facility located at the 88 Inch Cyclotron at Lawrence Berkeley National Laboratory in August 2011. A description of the experiment and results is given. The beam energy was initially chosen to be 39 MeV in order to measure an equivalent neutron energy range from 0 to 20 MeV. However, the proton beam was not stopped in the farady cup and the beam was deposited in the surrounding shielding material. The shielding material was not conductive, and a beam current, needed for proper tuning of the beam as well as experimental monitoring, could not be read. If the {sup 240}Am(n,f) surrogate experiment is to be run at LBNL, simple modifications to the beam collection site will need to be made. The beam energy was reduced to 29 MeV, which was within an energy regime of prior experiments and tuning conditions at STARS/LIBERACE. At this energy, the beam current was successfully tuned and measured. At 29 MeV, data was collected with both the {sup 243}Am and {sup 238}U targets. An example particle identification plot is shown in Fig. 1. The triton-fission coincidence rate for the {sup 243}Am target and {sup 238}U target were measured. Coincidence rates of 0.0233(1) cps and 0.150(6) cps were observed for the {sup 243}Am and {sup 238}U targets, respectively. The difference in count rate is largely attributed to the available target material - the {sup 238}U target contains approximately 7 times more atoms than the {sup 243}Am. A proton beam current of {approx}0.7 nA was used for measurements on both targets. Assuming a full experimental run under similar conditions, an estimate for the run time needed was made. Figure 2 shows the number of days needed as a function of acceptable uncertainty for a measurement of 1-20 MeV equivalent neutron energy, binned into 200 keV increments. A 5% measurement will take 3 days for U, but 20 days for Am. It may be difficult to be the sole user of the LBNL cyclotron, or another facility, for such an extended period. However, a 10% measurement will take 19 hours for U, and 5 days for Am. Such a run period is more reasonable and will allow for the first ever measurement of the {sup 240}Am(n,f) cross section. We also anticipate 40% more beam time being available at Texas A&M Cyclotron Institute compared to LBNL in FY2012. The increased amount of beam time will allow us to accumulate better statistics then what would have been available at LBNL

Low-mass fission detector for the fission neutron spectrum measurement

For the fission neutron spectrum measurement, the neutron energy is determined in a time-of-flight experiment by the time difference between the fission event and detection of the neutron. Therefore, the neutron energy resolution is directly determined by the time resolution of both neutron and fission detectors. For the fission detection, the detector needs not only a good timing response but also the tolerance of radiation damage and high {alpha}-decay rate. A parallel-plate avalanche counter (PPAC) has many advantages for the detection of heavy charged particles such as fission fragments. These include fast timing, resistance to radiation damage, and tolerance of high counting rate. A PPAC also can be tuned to be insensitive to particles, which is important for experiments with - emitting actinides. Therefore, a PPAC is an ideal detector for experiments requiring a fast and clean trigger for fission. In the following sections, the description will be given for the design and performance of a new low-mass PPAC for the fission-neutron spectrum measurements at LANL

Surrogate Measurement of the \u3csup\u3e238\u3c/sup\u3ePu(\u3cem\u3en,f\u3c/em\u3e\u3c/em\u3e) Cross Section

The neutron-induced fission cross section of 238Pu was determined using the surrogate ratio method. The (n,f) cross section over an equivalent neutron energy range 5–20 MeV was deduced from inelastic α-induced fission reactions on 239Pu, with 235U(α,α′f) and 236U(α,α′f) used as references. These reference reactions reflect 234U(n,f) and 235U(n,f) yields, respectively. The deduced 238Pu(n,f) cross section agrees well with standard data libraries up to ~10 MeV, although larger values are seen at higher energies. The difference at higher energies is less than 20%

Report on 241,242Am(n,x) surrogate cross section measurement

The main goal of this measurement is to determine the {sup 242}Am(n,f) and {sup 241}Am(n,f) cross sections via the surrogate {sup 243}Am. Gamma-ray data was also collected for the purpose of measuring the (n,2n) cross-sections. The experiment was conducted using the STARS/LIBERACE experimental facility located at the 88 Inch Cyclotron at Lawrence Berkeley National Laboratory the first week of February 2011. A description of the experiment and status of the data analysis follow

Commentary on the use of the reproduction number R during the COVID-19 pandemic

Since the beginning of the COVID-19 pandemic, the reproduction number R has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, R is defined as the average number of secondary infections caused by one primary infected individual. R seems convenient, because the epidemic is expanding if R>1 and contracting if R<1. The magnitude of R indicates by how much transmission needs to be reduced to control the epidemic. Using R in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of R but many, and the precise definition of R affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined R, there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate R vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when R is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of R, and the data and methods used to estimate it, can make R a more useful metric for future management of the epidemic

Fogarty International Center collaborative networks in infectious disease modeling:Lessons learnt in research and capacity building

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the ‘big data’ revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications. Keywords: Infectious diseases, Transmission models, Computational models, Pathogen evolution, Capacity building, Emerging disease threats, Influenza, Control, Polic