Integrating the little talks intervention into Early Head Start: An experimental examination of implementation supports involving fidelity monitoring and performance feedback

Enriching home visiting services by incorporating scientifically-supported interventions is a means for improving their effectiveness in promoting child development. However, deliberate efforts to ensure that home visitors are fully knowledgeable and supported to implement interventions with parents of young children are necessary. In this experimental study, a randomly-assigned group of Early Head Start home visitors monitored the fidelity of their provision of a scientifically-based intervention, Little Talks, and the program\u27s general child development services. On a bi-weekly basis, home visitors received performance feedback specific to their implementation of Little Talks and based upon the fidelity data. Findings demonstrated that home visitors showed immediate and consistent mastery of the Little Talks content, while the quality of their implementation, including their clinical decision-making and collaborative processes, improved to adequate levels over time. The Little Talks home visitors showed generalized improvements in their ability to obtain Parent Input while providing the program\u27s typical child development services were detected. In fact, Little Talks home visitors\u27 were superior in obtaining Parent Input relative to comparison home visitors. Further, parents for whom low-quality intervention implementation was observed discontinued their enrollment in home visiting prematurely, while high-quality implementation was associated with sustained enrollment. Limitations for this study are identified, leading to future directions for advancing home visitors\u27 incorporation of evidence-based practices

A Phase II Study of Sagopilone (ZK 219477; ZK-EPO) in Patients With Breast Cancer and Brain Metastases

Treatments for women with recurrent brain metastases from breast cancer are limited. In this phase II study, we administered sagopilone to patients with breast cancer and brain metastases. We observed modest activity with a central nervous system objective response rate of 13.3%; however, median PFS was disappointing. Further studies should focus on other agents to treat this challenging clinical problem

Dose patterns in commercially insured subjects chronically exposed to opioids: a large cohort study in the United States

<p>Abstract</p> <p>Background</p> <p>Little data exist on how opioid doses vary with the length of exposure among chronic opioid users.</p> <p>Methods</p> <p>To characterize the change in the dosage of opioids over time, a retrospective cohort study using the PharMetrics database for the years 1999 through 2008 was conducted. Individuals exposed to opioids in 2000 who had 2 opioid dispensings at least 6 months apart and were opioid naive (did not receive any opioid 6 month before their exposure in 2000) were included. The date of the first dispensing in 2000 was defined as the index date and the dispensing had to be for a strong and full agonist opioid. All opioid doses were converted to oral morphine equivalent doses. Exposure was classified as continuous or intermittent. Mean, median, interquartile range, and 95^thpercentile of opioid dose over 6-month periods, as well as the percentage of subjects who ever received a high or very high opioid dose, were calculated.</p> <p>Results</p> <p>Among the 48,986 subjects, the mean age was 44.5 years and 54.5% were women. Intermittent exposure was observed in 99% of subjects; continuous exposure was observed in 1% of subjects. The mean duration of exposure for the subjects who were continuously exposed to opioids was 477 days. In subjects with no cancer diagnosis who were continuously exposed to opioids, the mean, 25^th, 50^th, and 75^thpercentile of dose was stable during the first 2 years of use, but the 95^thpercentile increased. Seven percent of them were exposed to doses of 180 mg or more of morphine at some point.</p> <p>Conclusions</p> <p>Dose escalation is uncommon in subjects with intermittent exposure to opioids. For subjects with continuous exposure to opioids who have cancer, doses rise substantially with time. For those without cancer, doses remain relatively stable for the first 2 years of use, but subsequently increase. Seven percent of subjects with no cancer diagnosis will be exposed to daily doses of 180 mg or more of morphine equivalent at some point.</p

Serotonergic Contribution to Boys' Behavioral Regulation

Animal and human adult studies reveal a contribution of serotonin to behavior regulation. Whether these findings apply to children is unclear. The present study investigated serotonergic functioning in boys with a history of behavior regulation difficulties through a double-blind, acute tryptophan supplementation procedure.Participants were 23 boys (age 10 years) with a history of elevated physical aggression, recruited from a community sample. Eleven were given a chocolate milkshake supplemented with 500 mg tryptophan, and 12 received a chocolate milkshake without tryptophan. Boys engaged in a competitive reaction time game against a fictitious opponent, which assessed response to provocation, impulsivity, perspective taking, and sharing. Impulsivity was further assessed through a Go/No-Go paradigm. A computerized emotion recognition task and a staged instrumental help incident were also administered.Boys, regardless of group, responded similarly to high provocation by the fictitious opponent. However, boys in the tryptophan group adjusted their level of responding optimally as a function of the level of provocation, whereas boys in the control group significantly decreased their level of responding towards the end of the competition. Boys in the tryptophan group tended to show greater perspective taking, tended to better distinguish facial expressions of fear and happiness, and tended to provide greater instrumental help to the experimenter.The present study provides initial evidence for the feasibility of acute tryptophan supplementation in children and some effect of tryptophan supplementation on children's behaviors. Further studies are warranted to explore the potential impact of increased serotonergic functioning on boys' dominant and affiliative behaviors

The Stakes in Bayh-Dole: Public Values Beyond the Pace of Innovation

Evaluation studies of the Bayh-Dole Act are generally concerned with the pace of innovation or the transgressions to the independence of research. While these concerns are important, I propose here to expand the range of public values considered in assessing Bayh-Dole and formulating future reforms. To this end, I first examine the changes in the terms of the Bayh-Dole debate and the drift in its design. Neoliberal ideas have had a definitive influence on U.S. innovation policy for the last thirty years, including legislation to strengthen patent protection. Moreover, the neoliberal policy agenda is articulated and justified in the interest of “competitiveness.” Rhetorically, this agenda equates competitiveness with economic growth and this with the public interest. Against that backdrop, I use Public Value Failure criteria to show that values such as political equality, transparency, and fairness in the distribution of the benefits of innovation, are worth considering to counter the “policy drift” of Bayh-Dole

Variation in Surface Ionization Potentials of Pristine and Hydrated BiVO4

This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.The work was funded by the EPSRC (Grant No. EP/K004956/1) and the ERC (Grant No. 277757). The calculations used the ARCHER supercomputer through membership of the UK’s HPC Materials Chemistry Consortium (EPSRC Grant No. EP/L000202)

The Effectiveness of Incarceration-Based Drug Treatment on Criminal Behavior: A Systematic Review

Many, if not most, incarcerated offenders have substance abuse problems. Without effective treatment, these substance-abusing offenders are likely to persist in non-drug offending. The period of incarceration offers an opportunity to intervene in the cycle of drug abuse and crime. Although many types of incarceration-based drug treatment programs are available (e.g., therapeutic communities and group counseling), the effectiveness of these programs is unclear. The objective of this research synthesis is to systematically review quasi-experimental and experimental (RCT) evaluations of the effectiveness of incarceration-based drug treatment programs in reducing post-release recidivism and drug relapse. A secondary objective of this synthesis is to examine variation in effectiveness by programmatic, sample, and methodological features. In this update of the original 2006 review (see Mitchell, Wilson, and MacKenzie, 2006), studies made available since the original review were included in an effort to keep current with emerging research. This synthesis of evaluations of incarceration-based drug treatment programs found that such programs are modestly effective in reducing recidivism. These findings most strongly support the effectiveness of therapeutic communities, as these programs produced relatively consistent reductions in recidivism and drug use. Both counseling and incarceration-based narcotic maintenance programs had mixed effects. Counseling programs were associated with reductions in recidivism but not drug use; whereas, incarceration-based narcotic maintenance programs were associated with reductions in drug use but not recidivism. Note that our findings regarding the effectiveness of incarceration-based narcotic maintenance programs differ from a larger review of community-based narcotic maintenance programs (see Egli, Pina, Christensen, Aebi, and Killias, 2009). Finally, boot camp programs for drug offenders had negligible effects on both recidivism and drug use

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License