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Draft method C is a standardized method for quantifying E. coli densities in recreational waters using quantitative polymerase chain reaction (qPCR). The method includes a Microsoft Excel workbook that automatically screens for poor-quality data using a set of previously proposed acceptance criteria, generates weighted linear regression (WLR) composite standard curves, and calculates E. coli target gene copies in test samples. We compared standard curve parameter values and test sample results calculated with the WLR model to those from a Bayesian master standard curve (MSC) model using data from a previous multi-lab study. The two models’ mean intercept and slope estimates from twenty labs’ standard curves were within each other’s 95% credible or confidence intervals for all labs. E. coli gene copy estimates of six water samples analyzed by eight labs were highly overlapping among labs when quantified with the WLR and MSC models. Finally, we compared multiple labs’ 2016–2018 composite curves, comprised of data from individual curves where acceptance criteria were not used, to their corresponding composite curves with passing acceptance criteria. Composite curves developed from passing individual curves had intercept and slope 95% confidence intervals that were often narrower than without screening and an analysis of covariance test was passed more often. The Excel workbook WLR calculation and acceptance criteria will help laboratories implement draft method C for recreational water analysis in an efficient, cost-effective, and reliable manner.

Goucher and Briggs discuss the Department of Defense's (DoD) Program Objective Memorandum (POM). The POM is a request developed at the program level that shows how a department or program plans to allocate funds over a five-year period in accordance with the Defense Planning Guidance. A typical submission includes program requirements, prior year accomplishments, following year planned activities, schedule, requested budget, previously approved budget, the difference between approved and requested, and justification for the difference. At face value, this information is ideal for assessing budget needs across the DoD.

Anterior cruciate ligament (ACL) rupture is a common debilitating injury that can cause instability of the knee. We aimed to investigate the best management strategy between reconstructive surgery and non-surgical treatment for patients with a non-acute ACL injury and persistent symptoms of instability. We did a pragmatic, multicentre, superiority, randomised controlled trial in 29 secondary care National Health Service orthopaedic units in the UK. Patients with symptomatic knee problems (instability) consistent with an ACL injury were eligible. We excluded patients with meniscal pathology with characteristics that indicate immediate surgery. Patients were randomly assigned (1:1) by computer to either surgery (reconstruction) or rehabilitation (physiotherapy but with subsequent reconstruction permitted if instability persisted after treatment), stratified by site and baseline Knee Injury and Osteoarthritis Outcome Score—4 domain version (KOOS4). This management design represented normal practice. The primary outcome was KOOS4 at 18 months after randomisation. The principal analyses were intention-to-treat based, with KOOS4 results analysed using linear regression. This trial is registered with ISRCTN, ISRCTN10110685, and ClinicalTrials.gov, NCT02980367. Between Feb 1, 2017, and April 12, 2020, we recruited 316 patients. 156 (49%) participants were randomly assigned to the surgical reconstruction group and 160 (51%) to the rehabilitation group. Mean KOOS4 at 18 months was 73·0 (SD 18·3) in the surgical group and 64·6 (21·6) in the rehabilitation group. The adjusted mean difference was 7·9 (95% CI 2·5–13·2; p=0·0053) in favour of surgical management. 65 (41%) of 160 patients allocated to rehabilitation underwent subsequent surgery according to protocol within 18 months. 43 (28%) of 156 patients allocated to surgery did not receive their allocated treatment. We found no differences between groups in the proportion of intervention-related complications. Surgical reconstruction as a management strategy for patients with non-acute ACL injury with persistent symptoms of instability was clinically superior and more cost-effective in comparison with rehabilitation management. The UK National Institute for Health Research Health Technology Assessment Programme.

Rats were trained for multiple sessions in a place-conditioning shuttle-box to explore motivational interactions of mu and kappa opioid agonists, specifically fentanyl reward and spiradoline aversion. In Phase 1, groups of rats received various doses of mu or kappa agonists, or placebo, testing for preference or aversion. Group A always received saline SC before 15-minute sessions. Group B received fentanyl SC (0.003, 0.006, 0.012 mg/kg), Group C received low and medium doses of agonists SC, and Group D received spiradoline (0.3, 0.6, 1.2 mg/kg) SC during Training Sessions 1-4, rats being restricted to the drug-associated compartment. Rats received saline when restricted to the placebo-associate compartment and on test days with access to both shuttle-box compartments. In Phase 2 of the study, Training Session 5, Combinations of mu and kappa agonists were substituted in Groups B, C, and D. Dose-related preference to fentanyl and aversion to spiradoline occurred during Test Sessions 1-4. During Test Session 5, fentanyl preference in Group B was suppressed by spiradoline, rats in Group C had a saline-like response to combined agonists, and spiradoline aversion in Group D was attenuated by fentanyl. These findings suggest that combined doses of mu and kappa agonists, while additive for antinociception, offset the rewarding and punishing effects of each other.

The intelligence community needs software that can help them make sense of the information they receive through their work as analysts. The research in this paper establishes intelligence analysts as the use case for applying scenario-based design to generate a more user-centered, human-computer interaction experience. This can be accomplished through scenario scripts that make the cognitive processes and communication expectations from the user more explicit.This dissertation reviews historical trends among capabilities in prior software from other developers, and suggests applying sensemaking theory, participatory design, and scenario-based design to strengthen development of our software’s capabilities. Previous software for intelligence analysis has been discarded by the intelligence community because of two major factors: the difficulty of use for the software is too high, or the software does not meet the needs of the analyst. My research takes a different approach to conceptualizing and designing software for analysts. I propose a new methodology for developing software that will support analysts’ sensemaking by integrating educational and training materials that are already used by analysts in their own domain, such as those described by Beebe and Pherson (2014) or Hall and Citrenbaum (2009). I believe that this will create a more accessible interface and grant affordances that are already familiar to the user base. Providing analysts with a tool that affords a similar interface has proven to have some success with analysts as users, as has been discussed by Smallman (2017). The research in this dissertation focuses how I use the structured analytic technique of brainstorming as the cognitive basis that informs the creation of a digital tool.I use the brainstorming tool as the blueprint to explain users' cognitive schema, and then using scenario scripts, I propose ways to integrate the intended users’ cognitive processes into the development of our software through the lens of scenario-based design. My application of structured analytic techniques (which can be seen in Figure 1.1) is a new design approach to creating digital tools and software for intelligence analysis. I explain how these techniques are already utilized by intelligence analysts (Beebe & Pherson, 2014) and how these can be integrated into a cognitive and immersive environment to support multiple users and multimodal interactions in a way that is useful for intelligence analysis. Leveraging the capabilities of the cognitive and immersive room, I propose that our software enables a more collaborative and efficient sensemaking process. The specific research questions formulated for this stage of my research is: 1) How can I translate the analog environment of the intelligence analysts to a digital environment?, and 2) How can I incorporate existing tools, such as the sticky-notes tool, to help bolster sensemaking according to Pirolli and Card.The framework for my research is based on the intelligence analysis sensemaking process (see Figure 2.1), as explained by Pirolli and Card (2005). Integration of the respective iterative loops of foraging for information and sensemaking also informs how a user interface and the system tools should support the intelligence process. I also discuss the sensemaking qualities of the digital brainstorming tool compared to the analog tool from a user study covering nine sessions (n=26).

Fecal contamination is a common cause of impairment of surface waters. In monitoring studies, it is usually assessed by measuring concentrations of fecal indicator bacteria such as Escherichia coli (E. coli), a common monitoring target in freshwater systems. In this study, we assess the advantages and disadvantages of two common and previously validated methods for monitoring E. coli concentrations at freshwater beaches: Colilert-18®, with a turnaround time of ca. 18 h, and real-time quantitative PCR (qPCR), with a turnaround time of ca. 3–4 h. Based on data comprising 3081 pairs of Colilert-18 and qPCR estimates of E. coli concentrations in split samples from Michigan’s annual beach monitoring program in 2019 and 2020, we found that qPCR monitoring detected a high percentage of exceedances of the state’s water quality standard for E. coli contamination that went undetected on the day of sampling with Colilert-18 monitoring because qPCR concentration estimates were available on the day of sampling but Colilert-18 estimates were not. However, Colilert-18 data were more useful than qPCR data for the statistical comparison of contamination levels at different beaches, probably in part because Colilert-18 data showed a much lower percentage of concentration estimates outside the method’s range of quantification.

Michigan's water-quality standards specify that E. coli concentrations at bathing beaches must not exceed 300 E. coli per 100 mL, as determined by the geometric mean of culture-based concentrations in three or more representative samples from a given beach on a given day. Culture-based analyses require 18-24 h to complete, so results are not available for issuing beach notifications (advisories or closings) until the day following collection. This one-day delay is problematic because E. coli concentrations at beaches can change markedly from one day to the next. qPCR-based E. coli concentrations, by contrast, can be obtained in only 3-4 h, making same-day beach notifications possible. Michigan has proposed a qPCR threshold value (qTV) for E. coli of 1.863 log10 gene copies per reaction as a potential equivalent value to the state standard, based on statistical analyses of a set of training data from 2016-2018. The main purpose of the present study is to assess the validity of the proposed qTV by determining whether the implied qPCR-based beach notification decisions agree well with culture-based decisions on two sets of test data (from 2016-2018 and 2019-2020), and whether performance of the proposed threshold is similar on the test and training data. The results show that performance of the proposed qTV on both sets of test data was consistently good (e.g., 95% agreement with culture-based beach notification decisions during 2019-2020) and was at least as good as its performance on the training data set, supporting its use as an equivalent value to the state standard. Competing Interest Statement The authors have declared no competing interest. Footnotes * 1. An inconsistency in formatting author addresses was fixed. 2. The name of the Michigan Network for Environmental Health and Technology was corrected.

Mu and kappa opioid agonists are efficacious analgesics, especially against visceral pain. Interestingly, these opioids also produce various undesirable effects that seem to be in opposition to each other. Thus, the hypothesis that mu and kappa opioids in combination produced at least additive antinociception while reducing total side effects was tested. Regarding one of the side effects, mu-related euphoria, fentanyl and enadoline (mu-kappa opioid combination) demonstrated that a kappa agonist reduced the positive reinforcement (euphoria) of a mu agonist. Also, oxymorphone and butorphanol (mixed mu-kappa opioid combination) in the cat did not affect physiological parameters (respiratory rate, pulse rate, and mean arterial pressure) in analgesic dose levels. Results of antinociceptive studies indicated that mu and kappa opioid combinations in the colorectal distension (visceral model of pain/nociception) in rats and cats produced additive or synergistic interactions. In contrast, mu and kappa opioid combinations tested in the cold-water tail-flick (cutaneous thermal model of pain/nociception) in rats produced antagonistic interactions. These same opioids, tested individually, produced maximal levels of antinociception. Although mechanisms for antinociceptive interactions of mu and kappa opioids in colorectal distension and cold-water tail-flick are not fully understood, data from studies using antagonists (naloxone, beta-funaltrexamine, and norbinaltorphimine) and methadone-tolerant rats indicated that mu and kappa receptor activity was involved. In conclusion, mu and kappa opioid combinations produced at least additive visceral antinociception with minimal side effects.

The use of silicon nanostructures in solar cells offers a number of benefits, such as the fact they can be used on flexible substrates. A silicon wire-array structure, containing reflecting nanoparticles for enhanced absorption, is now shown to achieve 96% peak absorption efficiency, capturing 85% of light with only 1% of the silicon used in comparable commercial cells. Si wire arrays are a promising architecture for solar-energy-harvesting applications, and may offer a mechanically flexible alternative to Si wafers for photovoltaics 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 . To achieve competitive conversion efficiencies, the wires must absorb sunlight over a broad range of wavelengths and incidence angles, despite occupying only a modest fraction of the array’s volume. Here, we show that arrays having less than 5% areal fraction of wires can achieve up to 96% peak absorption, and that they can absorb up to 85% of day-integrated, above-bandgap direct sunlight. In fact, these arrays show enhanced near-infrared absorption, which allows their overall sunlight absorption to exceed the ray-optics light-trapping absorption limit 18 for an equivalent volume of randomly textured planar Si, over a broad range of incidence angles. We furthermore demonstrate that the light absorbed by Si wire arrays can be collected with a peak external quantum efficiency of 0.89, and that they show broadband, near-unity internal quantum efficiency for carrier collection through a radial semiconductor/liquid junction at the surface of each wire. The observed absorption enhancement and collection efficiency enable a cell geometry that not only uses 1/100th the material of traditional wafer-based devices, but also may offer increased photovoltaic efficiency owing to an effective optical concentration of up to 20 times.

Background A model for statewide dissemination of evidence-based treatment (EBT) for traumatized youth was piloted and taken to scale across North Carolina (NC). This article describes the implementation platform developed, piloted, and evaluated by the NC Child Treatment Program to train agency providers in Trauma-Focused Cognitive Behavioral Therapy using the National Center for Child Traumatic Stress Learning Collaborative (LC) Model on Adoption & Implementation of EBTs. This type of LC incorporates adult learning principles to enhance clinical skills development as part of training and many key implementation science strategies while working with agencies and clinicians to implement and sustain the new practice. Methods Clinicians ( n  = 124) from northeastern NC were enrolled in one of two TF-CBT LCs that lasted 12 months each. During the LC clinicians were expected to take at least two clients through TF-CBT treatment with fidelity and outcomes monitoring by trainers who offered consultation by phone and during trainings. Participating clinicians initiated treatment with 281 clients. The relationship of clinician and client characteristics to treatment fidelity and outcomes was examined using hierarchical linear regression. Results One hundred eleven clinicians completed general training on trauma assessment batteries and TF-CBT. Sixty-five clinicians met all mastery and fidelity requirements to meet roster criteria. One hundred fifty-six (55%) clients had fidelity-monitored assessment and TF-CBT. Child externalizing, internalizing, and post-traumatic stress symptoms, as well as parent distress levels, decreased significantly with treatment fidelity moderating child PTSD outcomes. Since this pilot, 11 additional cohorts of TF-CBT providers have been trained to these roster criteria. Conclusion Scaling up or outcomes-oriented implementation appears best accomplished when training incorporates: 1) practice-based learning, 2) fidelity coaching, 3) clinical assessment and outcomes-oriented treatment, 4) organizational skill-building to address barriers for agencies, and 5) linking clients to trained clinicians via an online provider roster. Demonstrating clinician performance and client outcomes in this pilot and subsequent cohorts led to legislative support for dissemination of a service array of EBTs by the NC Child Treatment Program.