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While great interest in health effects of natural product (NP) including dietary supplements and foods persists, promising preclinical NP research is not consistently translating into actionable clinical trial (CT) outcomes. Generally considered the gold standard for assessing safety and efficacy, CTs, especially phase III CTs, are costly and require rigorous planning to optimize the value of the information obtained. More effective bridging from NP research to CT was the goal of a September, 2018 transdisciplinary workshop. Participants emphasized that replicability and likelihood of successful translation depend on rigor in experimental design, interpretation, and reporting across the continuum of NP research. Discussions spanned good practices for NP characterization and quality control; use and interpretation of models (computational through in vivo) with strong clinical predictive validity; controls for experimental artefacts, especially for in vitro interrogation of bioactivity and mechanisms of action; rigorous assessment and interpretation of prior research; transparency in all reporting; and prioritization of research questions. Natural product clinical trials prioritized based on rigorous, convergent supporting data and current public health needs are most likely to be informative and ultimately affect public health. Thoughtful, coordinated implementation of these practices should enhance the knowledge gained from future NP research.

Background Latinxs are vastly underrepresented in mental health research; one of many contributing factors may be complexities in the research consent process, including language preferences. We examined determinants of comprehension of research consent procedures and tested the effects of a preconsent research schema condition among 180 adults with schizophrenia (60 Latinx-English and 60 Latinx-Spanish preference, and 60 non-Latinx White). Study Design Participants were randomly assigned (equal allocation) to an educational session regarding clinical research concepts and processes (schema condition) or to an attention control. Following a subsequent simulated consent procedure for a hypothetical drug trail, comprehension of consent disclosures was measured with 2 standard measures. Study Results One-way ANOVAs showed significant medium effect size differences between ethnicity/language groups on both measures of comprehension (η2s = 0.066–0.070). The Latinx-Spanish group showed lower comprehension than non-Latinx White participants; differences between the 2 Latinx groups did not reach statistical significance. Group differences were not statistically significant after adjusting for differences in education, or on scores from structured measures of acculturation, health literacy, or research literacy. Two-way ANOVAs showed no significant main effects for consent procedure on either comprehension measure (P s >.369; partial η2s < 0.006) and no significant group-by-consent interactions (P s >.554; partial η2s < 0.008). Conclusions Although the preconsent procedure was not effective, the results suggest health and research literacy may be targets for reducing disparities in consent comprehension. The onus is on researchers to improve communication of consent information as an important step to addressing health care disparities. [ABSTRACT FROM AUTHOR]

In 2004, The U.S. Department of Education stated, "Great schools have great leaders" (p.1). Research supported their declaration as our nation actively pursued educational reform. School systems across the country searched for good school principals to lead, believing they were the "cornerstones of good schools" (DiPaola & Tschannen-Moran, 2003, p. 43). In 2004, the U. S. Department of Education published national statistics, which summarized that there was a shortage of top-notch principals to meet the demands of No Child Left Behind. As schools struggled, they realized the problem did not lie in the quantity of school principals but in the quality of the principal and learning environments (2004). However, the real challenge became finding effective leaders to facilitate successful learning environments for teachers and students to learn. In the meantime, teachers became frustrated and angry with weak leadership and promised changes with no results. Schools experienced declining assessment scores, an increasing number of students who were experiencing school failure, dropping out, and in trouble with the law because of involvement in crime and violence. Therefore, research continues to be necessary to find what makes an effective leader and successful learning environment in high schools to quiet the "Angry Voices" (Brown, 2009). (Contains 1 table.)

This study compared relevant data and information about leadership and learning cultures in different sizes and types of high schools. Research was conducted using a quantitative design with a qualitative element. Quantitative data were gathered using a researcher-created survey. Independent sample t-tests were conducted to analyze the means of school size factors and learning structures. According to responses from teachers, statistically significant differences existed between small school size compared to shared and supportive leadership and collaborative culture. A one-way ANOVA (analysis of variance) was run to compare means of sub-scales shared and supportive leadership, collaborative culture, collective learning and application and shared values and vision. Implications for practice were suggested based upon literature and data. (Contains 4 tables and 1 figure.)

This paper argues for the application of Intersectional Feminist theoretical lens to social work pedagogy and provides sample educational activities, from simple to complex, which apply this lens to bachelors, masters, and doctoral social work levels. All social workers must be prepared to work effectively with transgender and gender nonconforming (TGNC) people. This work relies on social workers' knowledge, skills, and self-awareness. Social workers who lack knowledge, skills, and self-awareness of the dimensions of gender diversity can create barriers to care for TGNC people. Increasingly, research on social work education and professional social work organizations are encouraging and expanding guidelines for effective practices with TGNC people. Much of this guidance stresses discrete practices rather than situating practice within a broader theoretical framework. Rather than adding to general guidance, we advocate for the application of an Intersectional Feminist lens in teaching social workers dimensions of gender diversity. Applying an Intersectional Feminist lens to social work education creates a context to frame educational activities and facilitates students' self-discovery of personally and socially held conceptions and biases related to gender. We include activities to help those teaching social work infuse this lens into pedagogy across levels of teaching social work. [ABSTRACT FROM AUTHOR]

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]

To study tropical cyclones and generate forecast applications using satellite observations, researchers often consolidate disparate sources of raw and ancillary data. Data consolidation involves obtaining, collocating, and intercalibrating data from different sensors and derived products; calculating environmental diagnostics from a homogeneous source; and standardizing these various products for a straightforward analysis. To alleviate preprocessing issues and provide a long-term, global digital dataset of tropical cyclone satellite observations, we construct the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED contains tropical cyclone–centric 1) intercalibrated, multichannel, multisensor microwave brightness temperatures, 2) retrieved rainfall from NASA's Goddard Profiling Algorithm (GPROF), 3) nearly coincident geostationary satellite infrared brightness temperatures and derived metrics, 4) tropical cyclone position and intensity information, 5) ECMWF fifth-generation reanalysis fields and derived environmental diagnostics, and 6) precipitation radar observations from the TRMM and GPM Core Observatory satellites. TC PRIMED consists of over 176,000 overpasses of 2,101 storms from 1998 to 2019, providing researchers with an analysis-ready dataset to promote and support research into improving our understanding of the relationship between tropical cyclone convective and precipitation structure, intensity, and environment. Here, we briefly describe data sources and processing steps to create TC PRIMED. To demonstrate TC PRIMED's potential utility for studying important tropical cyclone processes and for application development, we present a shear-relative composite analysis of several multisensor satellite variables relative to the tropical cyclone lifetime maximum intensity. The composite analysis provides a simple example of how TC PRIMED can benefit future studies to advance our understanding of tropical cyclones and improve forecasts. [ABSTRACT FROM AUTHOR]