Search

Abstract The ability to accurately measure drug‐target interaction is critical for the discovery of new therapeutics. Classical pharmacological bioassays such as radioligand or fluorescent ligand binding assays can define the affinity or Kd of a ligand for a receptor with the lower the Kd, the stronger the binding and the higher the affinity. However, in many drug discovery laboratories today, the target of interest if often artificially upregulated by means of transfection to modify the host cell's genetic makeup. This then potentially invalidates the assumptions of classical pharmacology affinity calculations as the receptor of interest is no longer at normal physiological densities. The CXCR4 receptor is expressed on many different cancer cell types and is associated with metastasis and poor prognosis. Therefore, the CXCR4 receptor is a desirable target for novel therapeutics. In this study, we explore the applicability of the newly developed fluorescently tagged CXCR4 antagonists, IS4‐FAM as an investigative tool to study CXCR4 affinity and competitive antagonism in native, non‐transfected cancer cells using confocal microscopy and flow cytometry. IS4‐FAM directly labels CXCR4 in several cell lines including high CXCR4 expressing SK‐MEL‐28 (malignant melanoma) and PC3 (metastatic prostate cancer) and lower CXCR4 expressing THP‐1 (acute monocytic leukemia) and was competitive with the established CXCR4 antagonist, AMD3100. This highlights the potential of IS4‐FAM as a pharmacological tool for drug discovery in native cells lines and tissues.

Preterm birth is the leading cause of infant morbidity and mortality. There has been an interest in developing prostaglandin F2α (PGF2α) antagonists as a new treatment for preterm birth, although much of the rationale for their use is based on studies in rodents where PGF2α initiates labour by regressing the corpus luteum and reducing systemic progesterone concentrations. How PGF2α antagonism would act in humans who do not have a fall in systemic progesterone remains unclear. One possibility, in addition to an acute stimulation of contractions, is a direct alteration of the myometrial smooth muscle cell state towards a pro-labour phenotype. In this study, we developed an immortalised myometrial cell line, MYLA, derived from myometrial tissue obtained from a pregnant, non-labouring patient, as well as a novel class of PGF2α receptor (FP) antagonist. We verified the functionality of the cell line by stimulation with PGF2α, resulting in Gαq-specific coupling and Ca2+ release, which were inhibited by FP antagonism. Compared to four published FP receptor antagonists, the novel FP antagonist N582707 was the most potent compound [Fmax 7.67 ± 0.63 (IC50 21.26 nM), AUC 7.30 ± 0.32 (IC50 50.43 nM), and frequency of Ca2+ oscillations 7.66 ± 0.41 (IC50 22.15 nM)]. RNA-sequencing of the MYLA cell line at 1, 3, 6, 12, 24, and 48 h post PGF2α treatment revealed a transforming phenotype from a fibroblastic to smooth muscle mRNA profile. PGF2α treatment increased the expression of MYLK, CALD1, and CNN1 as well as the pro-labour genes OXTR, IL6, and IL11, which were inhibited by FP antagonism. Concomitant with the inhibition of a smooth muscle, pro-labour transition, FP antagonism increased the expression of the fibroblast marker genes DCN, FBLN1, and PDGFRA. Our findings suggest that in addition to the well-described acute contractile effect, PGF2α transforms myometrial smooth muscle cells from a myofibroblast to a smooth muscle, pro-labour–like state and that the novel compound N582707 has the potential for prophylactic use in preterm labour management beyond its use as an acute tocolytic drug.

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations from other sites to inform focal sites. In this paper, we evaluate two different DL model structures, a long short-term memory neural network (LSTM) and a recurrent graph convolutional neural network (RGCN), both with and without data assimilation for forecasting daily maximum stream temperature 7 days into the future at monitored and unmonitored locations in a 70-segment stream network. All our DL models performed well when forecasting stream temperature as the root mean squared error (RMSE) across all models ranged from 2.03 to 2.11°C for 1-day lead times in the validation period, with substantially better performance at gaged locations (RMSE = 1.45–1.52°C) compared to ungaged locations (RMSE = 3.18–3.27°C). Forecast uncertainty characterization was near-perfect for gaged locations but all DL models were overconfident (i.e., uncertainty bounds too narrow) for ungaged locations. Our results show that the RGCN with data assimilation performed best for ungaged locations and especially at higher temperatures (>18°C) which is important for management decisions in our study location. This indicates that the networked model structure and data assimilation techniques may help borrow information from nearby monitored sites to improve forecasts at unmonitored locations. Results from this study can help guide DL modeling decisions when forecasting other important environmental variables.

The ability to accurately measure drug‐target interaction is critical for the discovery of new therapeutics. Classical pharmacological bioassays such as radioligand or fluorescent ligand binding assays can define the affinity or Kd of a ligand for a receptor with the lower the Kd, the stronger the binding and the higher the affinity. However, in many drug discovery laboratories today, the target of interest if often artificially upregulated by means of transfection to modify the host cell's genetic makeup. This then potentially invalidates the assumptions of classical pharmacology affinity calculations as the receptor of interest is no longer at normal physiological densities. The CXCR4 receptor is expressed on many different cancer cell types and is associated with metastasis and poor prognosis. Therefore, the CXCR4 receptor is a desirable target for novel therapeutics. In this study, we explore the applicability of the newly developed fluorescently tagged CXCR4 antagonists, IS4‐FAM as an investigative tool to study CXCR4 affinity and competitive antagonism in native, non‐transfected cancer cells using confocal microscopy and flow cytometry. IS4‐FAM directly labels CXCR4 in several cell lines including high CXCR4 expressing SK‐MEL‐28 (malignant melanoma) and PC3 (metastatic prostate cancer) and lower CXCR4 expressing THP‐1 (acute monocytic leukemia) and was competitive with the established CXCR4 antagonist, AMD3100. This highlights the potential of IS4‐FAM as a pharmacological tool for drug discovery in native cells lines and tissues. [ABSTRACT FROM AUTHOR]

Abstract This article provides a commentary about the state of integrated, coordinated, open, and networked (ICON) principles in Earth and Planetary Surface Processes and discussion on the opportunities and challenges of adopting them. This commentary focuses on the challenges with current inclusive, equitable, and accessible science and highlights how research undertaken in the earth and planetary surface processes community currently benefit from and would be able to grow as a discipline with more directed implementation of ICON principles.

Evaluative instruments designed to assess a university's or college's institutionalization of service-learning practices have been devised and widely utilized. These instruments often come with assumptions about aligning data with decision making. To date, attempts to document community engagement practices within a department or an entire school/college within a comprehensive university are lacking in the literature. This article addresses that gap by presenting an approach that inventories faculty community engagement activities at the levels of a school and a department within a major research university. It also highlights the potential application of the results of this process for the decision-making of several audiences, ranging from institutional leaders seeking to strengthen outreach efforts to outreach staff looking to improve their programming. (Contains 5 figures, 3 tables and 2 endnotes.)

Understanding controls on solute export to streams is challenging because heterogeneous catchments can respond uniquely to drivers of environmental change. To understand general solute export patterns, we used a large‐scale inductive approach to evaluate concentration–discharge (C–Q) metrics across catchments spanning a broad range of catchment attributes and hydroclimatic drivers. We leveraged paired C–Q data for 11 solutes from CAMELS‐Chem, a database built upon an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous USA. Because C–Q relationships with Q thresholds reflect a shift in solute export dynamics and are poorly characterized across solutes and diverse catchments, we analysed C–Q relationships using Bayesian segmented regression to quantify Q thresholds in the C–Q relationship. Threshold responses were rare, representing only 12% of C–Q relationships, 56% of which occurred for solutes predominantly sourced from bedrock. Further, solutes were dominated by one or two C–Q patterns that reflected vertical solute–source distributions. Specifically, solutes predominantly sourced from bedrock had diluting C–Q responses in 43%–70% of catchments, and solutes predominantly sourced from soils had more enrichment responses in 35%–51% of catchments. We also linked C–Q relationships to catchment and hydroclimatic attributes to understand controls on export patterns. The relationships were generally weak despite the diversity of solutes and attribute types considered. However, catchment and hydroclimatic attributes in the central USA typically drove the most divergent export behaviour for solutes. Further, we illustrate how our inductive approach generated new hypotheses that can be tested at discrete, representative catchments using deductive approaches to better understand the processes underlying solute export patterns. Finally, given these long‐term C–Q relationships are from minimally disturbed catchments, our findings can be used as benchmarks for change in more disturbed catchments. [ABSTRACT FROM AUTHOR]

Climate change, including warmer winter temperatures, a shortened snowfall season, and more rain-on-snow events, threatens nordic skiing as a sport. In response, over-summer snow storage, attempted primarily using woodchips as a cover material, has been successfully employed as a climate change adaptation strategy by high-elevation and/or high-latitude ski centers in Europe and Canada. Such storage has never been attempted at a site that is both low elevation and midlatitude, and few studies have quantified storage losses repeatedly through the summer. Such data, along with tests of different cover strategies, are prerequisites to optimizing snow storage strategies. Here, we assess the rate at which the volume of two woodchip-covered snow piles (each ∼200 m3), emplaced during spring 2018 in Craftsbury, Vermont (45∘ N and 360 m a.s.l.), changed. We used these data to develop an optimized snow storage strategy. In 2019, we tested that strategy on a much larger, 9300 m3 pile. In 2018, we continually logged air-to-snow temperature gradients under different cover layers including rigid foam, open-cell foam, and woodchips both with and without an underlying insulating blanket and an overlying reflective cover. We also measured ground temperatures to a meter depth adjacent to the snow piles and used a snow tube to measure snow density. During both years, we monitored volume change over the melt season using terrestrial laser scanning every 10–14 d from spring to fall. In 2018, snow volume loss ranged from 0.29 to 2.81 m3 d−1, with the highest rates in midsummer and lowest rates in the fall; mean rates of volumetric change were 1.24 and 1.50 m3 d−1, 0.55 % to 0.72 % of initial pile volume per day. Snow density did increase over time, but most volume loss was the result of melting. Wet woodchips underlain by an insulating blanket and covered with a reflective sheet were the most effective cover combination for minimizing melt, likely because the aluminized surface reflected incoming short-wave radiation while the wet woodchips provided significant thermal mass, allowing much of the energy absorbed during the day to be lost by long-wave emission at night. The importance of the pile surface-area-to-volume ratio is demonstrated by 4-fold lower rates of volumetric change for the 9300 m3 pile emplaced in 2019; it lost  % of its initial volume per day between April and October, retaining ∼60 % of the initial snow volume over summer. Together, these data demonstrate the feasibility of over-summer snow storage at midlatitudes and low elevations and suggest efficient cover strategies.

Excessive streambank erosion is a significant source of fine sediments and associated nutrients in many river systems as well as poses risk to infrastructure. Geomorphic change detection using high-resolution topographic data is a useful method for monitoring the extent of bank erosion along river corridors. Recent advances in an unmanned aircraft system (UAS) and structure from motion (SfM) photogrammetry techniques allow acquisition of high-resolution topographic data, which are the methods used in this study. To evaluate the effectiveness of UAS-based photogrammetry for monitoring bank erosion, a fixed-wing UAS was deployed to survey 20 km of river corridors in central Vermont, in the northeastern United States multiple times over a two-year period. Digital elevation models (DEMs) and DEMs of difference allowed quantification of volumetric changes along selected portions of the survey area where notable erosion occurred. Results showed that UAS was capable of collecting high-quality topographic data at fine resolutions even along vegetated river corridors provided that the surveys were conducted in early spring, after snowmelt but prior to summer vegetation growth. Longer term estimates of streambank movements using the UAS showed good comparison to previously collected airborne lidar surveys and allowed reliable quantification of significant geomorphic changes along rivers.

Preterm birth is the leading cause of infant morbidity and mortality. There has been an interest in developing prostaglandin F2α (PGF2α) antagonists as a new treatment for preterm birth, although much of the rationale for their use is based on studies in rodents where PGF2α initiates labour by regressing the corpus luteum and reducing systemic progesterone concentrations. How PGF2α antagonism would act in humans who do not have a fall in systemic progesterone remains unclear. One possibility, in addition to an acute stimulation of contractions, is a direct alteration of the myometrial smooth muscle cell state towards a pro-labour phenotype. In this study, we developed an immortalised myometrial cell line, MYLA, derived from myometrial tissue obtained from a pregnant, non-labouring patient, as well as a novel class of PGF2α receptor (FP) antagonist. We verified the functionality of the cell line by stimulation with PGF2α, resulting in Gαq-specific coupling and Ca2+ release, which were inhibited by FP antagonism. Compared to four published FP receptor antagonists, the novel FP antagonist N582707 was the most potent compound [Fmax 7.67 ± 0.63 (IC50 21.26 nM), AUC 7.30 ± 0.32 (IC50 50.43 nM), and frequency of Ca2+ oscillations 7.66 ± 0.41 (IC50 22.15 nM)]. RNA-sequencing of the MYLA cell line at 1, 3, 6, 12, 24, and 48 h post PGF2α treatment revealed a transforming phenotype from a fibroblastic to smooth muscle mRNA profile. PGF2α treatment increased the expression of MYLK, CALD1, and CNN1 as well as the pro-labour genes OXTR, IL6, and IL11, which were inhibited by FP antagonism. Concomitant with the inhibition of a smooth muscle, pro-labour transition, FP antagonism increased the expression of the fibroblast marker genes DCN, FBLN1, and PDGFRA. Our findings suggest that in addition to the well-described acute contractile effect, PGF2α transforms myometrial smooth muscle cells from a myofibroblast to a smooth muscle, pro-labour–like state and that the novel compound N582707 has the potential for prophylactic use in preterm labour management beyond its use as an acute tocolytic drug. [ABSTRACT FROM AUTHOR]

Moving on from literature that focuses on how consumers use social media and the benefits of organizations utilizing platforms for health and risk communication, this study explores how specific characteristics of tweets affect the way in which they are perceived. An online survey with 251 participants with self-reported food hypersensitivity (FH) took part in an online experiment to consider the impact of tweet characteristics on perceptions of source credibility, message credibility, persuasiveness, and intention to act upon the presented information. Positioning the research hypotheses within the framework of the Elaboration Likelihood Model and Uses and Gratifications Theory, the study explored motivations for using social media and tested the impact of the affordances of Twitter—(1) the inclusion of links and (2) the number of social validation indicators (likes and retweets). Having links accompanying tweets significantly increased ratings of the tweets’ message credibility, as well as persuasiveness of their content. Socially validated tweets had no effect on these same variables. Parents of FH children were found to utilize social media for social reasons more than hypersensitive adults; concern level surrounding a reaction did not appear to alter the level of use. Links were considered valuable in obtaining social media users to attend to useful or essential food health and risk information. Future research in this area can usefully consider the nature and the effects of social validation in relation to other social media platforms and with other groups.

Opening data promises to improve research rigour and democratize knowledge production. But it also presents practical, theoretical, and ethical considerations for qualitative researchers in particular. Discussion about open data in qualitative social psychology predates the replication crisis. However, the nuances of this ongoing discussion have not been translated into current journal guidelines on open data. In this article, we summarize ongoing debates about open data from qualitative perspectives, and through a content analysis of 261 journals we establish the state of current journal policies for open data in the domain of social psychology. We critically discuss how current common expectations for open data may not be adequate for establishing qualitative rigour, can introduce ethical challenges, and may place those who wish to use qualitative approaches at a disadvantage in peer review and publication processes. We advise that future open data guidelines should aim to reflect the nuance of arguments surrounding data sharing in qualitative research, and move away from a universal "one‐size‐fits‐all" approach to data sharing. This article outlines the past, present, and the potential future of open data guidelines in social‐psychological journals. We conclude by offering recommendations for how journals might more inclusively consider the use of open data in qualitative methods, whilst recognizing and allowing space for the diverse perspectives, needs, and contexts of all forms of social‐psychological research. [ABSTRACT FROM AUTHOR]