Authentication of reprocessing plant safeguards data through correlation analysis

This report investigates the feasibility and benefits of two new approaches to the analysis of safeguards data from reprocessing plants. Both approaches involve some level of plant modeling. All models involve some form of mass balance, either applied in the usual way that leads to material balances for individual process vessels at discrete times or applied by accounting for pipe flow rates that leads to material balances for individual process vessels at continuous times. In the first case, material balances are computed after each tank-to-tank transfer. In the second case, material balances can be computed at any desired time. The two approaches can be described as follows. The first approach considers the application of a new multivariate sequential test. The test statistic is a scalar, but the monitored residual is a vector. The second approach considers the application of recent nonlinear time series methods for the purpose of empirically building a model for the expected magnitude of a material balance or other scalar variable. Although the report restricts attention to monitoring scalar time series, the methodology can be extended to vector time series

Combined cognitive and vocational interventions after mild to moderate traumatic brain injury: study protocol for a randomized controlled trial

Background A considerable proportion of patients with mild to moderate traumatic brain injury (TBI) experience long-lasting somatic, cognitive, and emotional symptoms that may hamper their capacity to return to work (RTW). Although several studies have described medical, psychological, and work-related factors that predict RTW after TBI, well-controlled intervention studies regarding RTW are scarce. Furthermore, there has traditionally been weak collaboration among health-related rehabilitation services, the labor and welfare sector, and workplaces. Methods/design This study protocol describes an innovative randomized controlled trial in which we will explore the effect of combining manualized cognitive rehabilitation (Compensatory Cognitive Training [CCT]) and supported employment (SE) on RTW and related outcomes for patients with mild to moderate TBI in real-life competitive work settings. The study will be carried out in the southeastern region of Norway and thereby be performed within the Norwegian welfare system. Patients aged 18–60 years with mild to moderate TBI who are employed in a minimum 50% position at the time of injury and sick-listed 50% or more for postconcussive symptoms 2 months postinjury will be included in the study. A comprehensive assessment of neurocognitive function, self-reported symptoms, emotional distress, coping style, and quality of life will be performed at baseline, immediately after CCT (3 months after inclusion), following the end of SE (6 months after inclusion), and 12 months following study inclusion. The primary outcome measures are the proportion of participants who have returned to work at 12-month follow-up and length of time until RTW, in addition to work stability as well as work productivity over the first year following the intervention. Secondary outcomes include changes in self-reported symptoms, emotional and cognitive function, and quality of life. Additionally, a qualitative RTW process evaluation focused on organizational challenges at the workplace will be performed. Discussion The proposed study will combine cognitive and vocational rehabilitation and explore the efficacy of increased cross-sectoral collaboration between specialized health care services and the labor and welfare system. If the intervention proves effective, the project will describe the cost-effectiveness and utility of the program and thereby provide important information for policy makers. In addition, knowledge about the RTW process for persons with TBI and their workplaces will be provided. Trial registration ClinicalTrials.gov, NCT03092713. Registered on 10 March 2017

Development of a PROTAC-Based Targeting Strategy Provides a Mechanistically Unique Mode of Anti-Cytomegalovirus Activity

Human cytomegalovirus (HCMV) is a major pathogenic herpesvirus that is prevalent worldwide and it is associated with a variety of clinical symptoms. Current antiviral therapy options do not fully satisfy the medical needs; thus, improved drug classes and drug-targeting strategies are required. In particular, host-directed antivirals, including pharmaceutical kinase inhibitors, might help improve the drug qualities. Here, we focused on utilizing PROteolysis TArgeting Chimeras (PROTACs), i.e., hetero-bifunctional molecules containing two elements, namely a target-binding molecule and a proteolysis-inducing element. Specifically, a PROTAC that was based on a cyclin-dependent kinase (CDK) inhibitor, i.e., CDK9-directed PROTAC THAL-SNS032, was analyzed and proved to possess strong anti-HCMV AD169-GFP activity, with values of EC50 of 0.030 µM and CC50 of 0.175 µM (SI of 5.8). Comparing the effect of THAL-SNS032 with its non-PROTAC counterpart SNS032, data indicated a 3.7-fold stronger anti-HCMV efficacy. This antiviral activity, as illustrated for further clinically relevant strains of human and murine CMVs, coincided with the mid-nanomolar concentration range necessary for a drug-induced degradation of the primary (CDK9) and secondary targets (CDK1, CDK2, CDK7). In addition, further antiviral activities were demonstrated, such as the inhibition of SARS-CoV-2 replication, whereas other investigated human viruses (i.e., varicella zoster virus, adenovirus type 2, and Zika virus) were found insensitive. Combined, the antiviral quality of this approach is seen in its (i) mechanistic uniqueness; (ii) future options of combinatorial drug treatment; (iii) potential broad-spectrum activity; and (iv) applicability in clinically relevant antiviral models. These novel data are discussed in light of the current achievements of anti-HCMV drug development

Bi-allelic genetic variants in the translational GTPases GTPBP1 and GTPBP2 cause a distinct identical neurodevelopmental syndrome

The homologous genes GTPBP1 and GTPBP2 encode GTP-binding proteins 1 and 2, which are involved in ribosomal homeostasis. Pathogenic variants in GTPBP2 were recently shown to be an ultra-rare cause of neurodegenerative or neurodevelopmental disorders (NDDs). Until now, no human phenotype has been linked to GTPBP1. Here, we describe individuals carrying bi-allelic GTPBP1 variants that display an identical phenotype with GTPBP2 and characterize the overall spectrum of GTP-binding protein (1/2)-related disorders. In this study, 20 individuals from 16 families with distinct NDDs and syndromic facial features were investigated by whole-exome (WES) or whole-genome (WGS) sequencing. To assess the functional impact of the identified genetic variants, semi-quantitative PCR, western blot, and ribosome profiling assays were performed in fibroblasts from affected individuals. We also investigated the effect of reducing expression of CG2017, an ortholog of human GTPBP1/2, in the fruit fly Drosophila melanogaster. Individuals with bi-allelic GTPBP1 or GTPBP2 variants presented with microcephaly, profound neurodevelopmental impairment, pathognomonic craniofacial features, and ectodermal defects. Abnormal vision and/or hearing, progressive spasticity, choreoathetoid movements, refractory epilepsy, and brain atrophy were part of the core phenotype of this syndrome. Cell line studies identified a loss-of-function (LoF) impact of the disease-associated variants but no significant abnormalities on ribosome profiling. Reduced expression of CG2017 isoforms was associated with locomotor impairment in Drosophila. In conclusion, bi-allelic GTPBP1 and GTPBP2 LoF variants cause an identical, distinct neurodevelopmental syndrome. Mutant CG2017 knockout flies display motor impairment, highlighting the conserved role for GTP-binding proteins in CNS development across species

Pet Project or Best Project? Online Decision Support Tools for Prioritizing Barrier Removals in the Great Lakes and Beyond

Structures that block movement of fish through river networks are built to serve a variety of societal needs, including transportation, hydroelectric power, and exclusion of exotic species. Due to their abundance, road crossings and dams reduce the amount of habitat available to fish that migrate from the sea or lakes into rivers to breed. The benefits to fish of removing any particular barrier depends on its location within the river network, its passability to fish, and the relative position of other barriers within the network. Balancing the trade-offs between ecological and societal values makes choosing among potential removal projects difficult. To facilitate prioritization of barrier removals, we developed an online decision support tool (DST) with three functions: (1) view existing barriers at various spatial scales; (2) modify information about barriers, including removal costs; and (3) run optimization models to identify portfolios of removals that provide the greatest amount of habitat access for a given budget. A survey of available DSTs addressing barrier removal prioritization indicates that barrier visualization is becoming widespread but few tools allow dynamic calculation of connectivity metrics, scenario analysis, or optimization. Having these additional functions, our DST enables organizations to develop barrier removal priorities based on cost-effectiveness in restoring aquatic connectivity

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect loss of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values

Solution monitoring: Quantitative benefits to safeguards

This paper investigates how SM (essentially continuous monitoring of solution level, density, and temperature in all key process tanks) can improve loss detection in a formal statistical sense. The authors use a simulation code developed at Los Alamos (FACSIM) to simulate data from the Rokkasho Reprocessing Plant (RRP), which is now under construction in Japan. They show the possibility of reducing the effect of systematic errors by using solution monitoring data to calculate bias corrections. They divide the effort into 4 activities for a 3-tank system and for a more realistic 15-tank system. The authors say a tank is in wait mode if its level change is due only to measurement error. Otherwise, a tank is in transfer mode. The 4 activities are (1) monitor each tank for volume loss during each wait mode, (2) monitor each tank for mass loss during each wait mode, (3) monitor each tank for volume loss during each transfer mode, and (4) monitor each tank for mass loss during each transfer mode. The success of this effort will depend largely on the performance of the authors` proposed bias corrections to the volume measurements. To apply bias corrections, the authors require that some reasonable number of transfers (for example, 20) are known to have no true loss. The effectiveness of the technique will depend on the relative sizes of the random and systematic errors involved because the main outcome is a reduction of the systematic error variances. If there are large variations in true (legitimate) temporary losses such as pipe holdup that would add to the random error variance in this model, the effectiveness will be reduced. Even in such cases the authors show there can be improved protracted loss detection and will definitely be improved abrupt loss detection