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The use of anabolic androgenic steroids (AAS), such as testosterone, is associated with a myriad of physiological and behavioral health concerns. Most notably, in both humans and rats, exposure to supraphysiological levels of testosterone has been shown to increase aggressive-like behaviors. However, it is unclear if testosterone enhances the motivation to engage in aggressive-type behaviors to begin with. To elucidate the effects of testosterone on the motivation to seek out a potentially aggressive encounter, rats were tested on a social motivation task in which physical contact between test and stimulus rats was prevented by a wire barrier, which eliminated the possibility of physical provocation. Relative to vehicle treated rats, rats treated with testosterone during adolescence exhibited lower levels of social motivation, as indicted by a reduction in the amount of time spent adjacent to the wire barrier. Over the course of the social motivation test, there were no differences between testosterone and vehicle treated rats in the frequency of rearing behavior and the total number of fecal boli, which suggests that the testosterone-induced reduction in social motivation occurred independent of changes in activity and emotionality, respectively. Consistent with previous studies, testosterone treatment during adolescence decreased total weight gain, increased the weights of the bulbourethral glands, and decreased the weights of the testes. These results suggest that the heightened levels of aggression following testosterone exposure that have been reported in other studies are likely not due to a greater motivation to seek out an interaction with a conspecific of the same sex. Abbreviations: ASS – Anabolic Androgenic Steroids; PND – Post Natal Day; HPA axis – Hypothalamic-Pituitary-Adrenal axis; HPG axis – Hypothalamic-Pituitary-Gonad axis

Although many species shift their phenology with climate change, species vary significantly in the direction and magnitude of these responses (i.e., phenological sensitivity). Studies increasingly detect early phenology or high phenological sensitivity to climate in non-native species, which may favor non-native species over natives in warming climates. Yet relatively few studies explicitly compare phenological responses to climate between native vs. non-native species or between non-native populations in the native vs. introduced range, limiting our ability to quantify the role of phenology in invasion success. Here, we review the empirical evidence for and against differences in phenology and phenological sensitivity to climate in both native vs. non-native species and native and introduced populations of non-native species. Contrary to common assumptions, native and non-native plant species did not consistently differ in mean phenology or phenological sensitivity. However, non-native plant species were often either just as or more sensitive, but rarely less sensitive, to climate as natives. Introduced populations of non-native plant species often show earlier reproduction than native populations of the same species, but there was mixed evidence for differences in phenological sensitivity between introduced and native plant populations. We found very few studies comparing native vs. invasive animal phenology. Future work should characterize phenological sensitivity to climate in native vs. non-native plant and animal species, in native vs. introduced populations of non-native species, and across different stages of invasion, and should carefully consider how differences in phenology might promote invasion success or disadvantage native species under climate change.

Abstract Sex ratios affect population dynamics and individual fitness, and changing sex ratios can be indicative of shifts in sex‐specific survival at different life stages. While climate and landscape changes alter sex ratios of wild bird populations, long‐term, landscape scale assessments of sex ratios are rare. Further, little work has been done to understand changes in sex ratios in avian communities. In this manuscript, we analyze long‐term (1961–2015) data on five species of ducks across five broad climatic regions of the United States to estimate the effects of drought and long‐term trends on the proportion of juvenile females captured at banding. As waterfowl have a 1:1 sex ratio at hatch, we interpret changes in sex ratios of captured juveniles as changes in sex‐specific survival rates during early life. Seven of 12 species‐region pairs exhibited evidence for long‐term trends in the proportion of juvenile females at banding. The proportion of juvenile females at banding increased for duck populations in the western United States and typically declined for duck populations in the eastern United States. We only observed evidence for an effect of drought in two of the 12 species‐region pairs, where the proportion of females declined during drought. As changes to North American landscapes and climate continue and intensify, we expect continued changes in sex‐specific juvenile survival rates. More broadly, we encourage further research examining the mechanisms underlying long‐term trends in juvenile sex ratios in avian communities.

Machine Learning (ML) techniques have been effectively used to learn the intricate relationships between variables that play a significant role in the field of engine design. However, there are two challenges to this approach – (1) Identifying ML regression models that could capture the trends of a response variable, given a non-parametric training set of relatively small size, with an acceptable accuracy and response time, and (2) identifying the size of the dataset, with respect to the input parameters, to be used for training and validation of the ML models. There is not enough information in the literature to reach a consensus on the sampling size to be used for an engine design problem that would yield an acceptable measure of goodness-of-fit. This is evident from the varied size of the training/validation data size used within the engine research community, as will be elaborated on in the following sections. The objective of this paper is to provide an insight into the sampling size required by the different ML models to achieve an acceptable fit between the model and the data, to be used in three types of engine design/optimization problems – (1) conventional diesel combustion (CDC) engine performance over a wide range of speed and load, (2) cold-start operation of a direct-injected spark-ignition (DISI) engine, and (3) high-load performance of a dual-fuel reactivity-controlled compression ignition (RCCI) engine. [ABSTRACT FROM AUTHOR]

Computational Fluid Dynamics (CFD) modeling of gasoline spark-ignited engine combustion has been extensively discussed using both detailed chemistry mechanisms (e.g., SAGE) and flamelet models (e.g., the G -equation). The models have been extensively validated under normal operating conditions; however, few studies have discussed the capability of these models in capturing DISI combustion under cold-start conditions. A cold-start differs from normal operating conditions in various respects, such as (1) having highly retarded spark timing to help generate a high heat flux in the exhaust for a rapid catalyst light-off; (2) having split-injection strategies to ensure a favorable stratification at the vicinity of the spark plug and reduced film formation; and (3) having optimized valve timings for reduced NOx emissions via increased internal residuals and reduced hydrocarbon (HC) emissions via prolonged oxidation of the combustion products. The retarded spark timing introduces the adverse effect of a decaying turbulence field, which results in a reduced turbulent flame speed. The analysis of all these factors happening inside the cylinder appears complicated at first glance; however, it could be made possible by efficient use of the existing CFD models. The current study explored the capability of the SAGE detailed chemistry model in capturing cold-start flame travel in a DISI engine. The results were then compared against the G -equation-based GLR model, which has been validated for excellent predictions of the DISI cold-start combustion as shown by Ravindran et al. The flame travel was captured on a Borghi-Peters diagram to find that the flame travels through corrugated, wrinkled, and laminar regimes. In order to fully evaluate the capability of the detailed chemistry model in predicting such changing turbulence-chemistry interactions, it will need to be studied individually in each regime; however, the scope of the current paper is limited to the study of the model behavior in the laminar regime, which will be shown to be important for DISI engine cold-start. The SAGE detailed chemistry model, with a toluene reference fuel (TRF) mechanism validated for gasoline laminar flame speeds, was found to significantly under-predict the flame propagation speeds because of the effects of numerical viscosity and discrepancies in capturing molecular diffusion. The causes and effects of this under-prediction and the ways in which this can be improved are presented in the paper. [ABSTRACT FROM AUTHOR]

To accurately model the Direct Injection Spark Ignition (DISI) combustion process, it is important to account for the effects of the spark energy discharge process. The proximity of the injected fuel spray and spark electrodes leads to steep gradients in local velocities and equivalence ratios, particularly under cold-start conditions when multiple injection strategies are employed. The variations in the local properties at the spark plug location play a significant role in the growth of the initial flame kernel established by the spark and its subsequent evolution into a turbulent flame. In the present work, an ignition model is presented that is compatible with the G-Equation combustion model, which responds to the effects of spark energy discharge and the associated plasma expansion effects. The model is referred to as the Plasma Velocity on G-surface (PVG) model, and it uses the G-surface to capture the early kernel growth. The model derives its theory from the Discrete Particle Ignition (DPIK) model, which accounts for the effects of electrode heat transfer, spark energy, and chemical heat release from the fuel on the early flame kernel growth. The local turbulent flame speed has been calculated based on the instantaneous location of the flame kernel on the Borghi-Peters regime diagram. The model has been validated against the experimental measurements given by Maly and Vogel,1 and the constant volume flame growth measurements provided by Nwagwe et al.2 Multi-cycle simulations were performed in CONVERGE3 using the PVG ignition model in combination with the G-Equation-based GLR4 model in a RANS framework to capture the combustion characteristics of a DISI engine. Good agreements with the experimental pressure trace and apparent heat-release rates were obtained. Additionally, the PVG ignition model was observed to substantially reduce the sensitivity of the default G-sourcing ignition method employed by CONVERGE. [ABSTRACT FROM AUTHOR]

The effect of triple injection strategies in a diesel engine to reduce cold start emissions were experimentally and computationally investigated in this work. The experiments were performed using a 1.9 L four-cylinder, turbocharged compression ignition engine with diesel fuel. As a representative of the cold start condition in the diesel engine, a low load condition of 1500 rpm and 2 bar brake mean effective pressure (BMEP) was chosen as the fixed condition for this study. The injection strategy made use of a late post injection to reduce catalyst light-off time. The pilot and main injections are fixed and the post injection timing is swept later into the expansion stroke to increase exhaust enthalpy available to heat the aftertreatment system. To further understand the source of hydrocarbon (HC) emissions from late injections, equivalent experiments were conducted with a mixture of n-heptane and iso-octane that matched the reactivity of the diesel fuel. The mixture of primary reference fuels (i.e., PRF 34) obtained by matching the cetane number (CN) of diesel fuel, showed similar combustion characteristics of diesel, but is much more volatile due to lighter components in the PRF mixture. The increased volatility of PRF 34 suppressed liquid fuel impingement on the cylinder liner, which isolated liner impingement as a possible source of HC emissions. Simulations were also performed for the present engine configuration and operating conditions in a sector mesh using CONVERGE™. The physical properties of diesel fuel were modeled using a five-component surrogate. The chemical kinetics of the diesel fuel were modeled with a reduced n-heptane model. The simulations were able to capture the experimental trends of combustion characteristics and emissions. The HC emissions were observed to increase for both fuels with retarded post injection timings in engine experiments. PRF 34 had comparable HC emissions to diesel experiments, which indicated that the liner impingement is not the main source of the increase in HC emissions. Overmixing of the fuel and air was identified as the major cause of increase in HC emissions. Additionally, exhaust gas recirculation (EGR) also intensified the overmixing phenomenon and thereby increase in HC emissions. [ABSTRACT FROM AUTHOR]

Aim: Social isolation in cancer patients is correlated with prognosis and is a potential mediator of treatment completion. Black women with endometrial cancer (EC) are at increased risk for social isolation when compared with White patients. We developed the Social Interventions for Support during Treatment for Endometrial Cancer and Recurrence (SISTER) study to compare and evaluate interventions to address social isolation among Black women with high-risk EC in USA. The primary objective of the SISTER study is to determine whether virtual support interventions improve treatment completion compared with Enhanced Usual Care. Secondary objectives include comparing effectiveness virtual evidence-based interventions and evaluating barriers and facilitators to social support delivery. Patients & methods: This is a multi-site prospective, open-label, community-engaged randomized controlled trial, consisting of three intervention arms: enhanced usual care, facilitated support group and one-to-one peer support. Primary outcome will be measured using relative dose. Qualitative semi-structured interviews will be conducted with a subset of participants to contextualize the relative degree or lack thereof of social isolation, over time. Data analysis: Primary analysis will be based on an intent-to-treat analysis. Multivariable analysis will be performed to determine the effect of the intervention on the primary and secondary outcomes of interest, relative dose and social isolation score. Semi-structured interviews will be qualitatively analyzed using inductive and deductive approaches of content analysis. Discussion/conclusion: Endometrial cancer mortality disproportionately affects Black women, and social isolation contributes to this disparity. The SISTER study aims to identify whether and to what extent differing social support vehicles improve key outcomes for Black women in the United States with high-risk EC. Clinical Trial Registration: NCT04930159 (ClinicalTrials.gov).

Background: Provider uncertainty about the appropriate guideline-concordant evaluation of endometrial cancer (EC) symptoms may be a factor in racial inequities in EC. To evaluate the relationship between EC knowledge and reported practice patterns in a nationally representative survey of first-line providers for initial EC symptoms. Materials and Methods: This was a mailed cross-sectional survey of physicians and nurse practitioners from professional organization roster of providers from Obstetrics and Gynecology (OBGYN), Family Medicine, Internal Medicine, and Emergency Medicine. It queried demographics, practice characteristics, EC knowledge, and guideline-concordant practice patterns via three case vignettes. Regions of low response were retargeted to ensure strong representation among providers caring for Black women patients. EC knowledge was analyzed via a composite score (range: -3 to 10, with higher scores representing more EC knowledge), and adjusted prevalence ratios (PRs) used to test the association between knowledge and reported practice patterns. Results: Among 531 returned surveys (response rate = 38%), OBGYN had highest (53%) frequency of >6 (median) EC knowledge score, and Emergency Medicine had the lowest (15%) ( p  < 0.001). Nonguideline-concordant practice patterns were reported in 14%, 41%, and 35% of the three EC cases presented. Providers with knowledge >6, ( n  = 205) were significantly more likely to report guideline-concordant care on case vignettes (PR 1.28-1.36). Conclusions: In a national survey of multi-specialty backgrounds, there were basic knowledge gaps about EC and EC risk factors among providers, and a sizeable proportion reported nonguideline concordant practices. These findings indicate the importance of targeted education and training for first-line providers, as EC incidence rises.

Objective: Underrepresented groups may be dissuaded from clinical trial participation without perceived value. We therefore comprehensively assessed gynecologic cancer clinical trial protocols for the inclusion of items of value most important to Black individuals., Methods: ClinicalTrials.gov was queried for NCI-sponsored gynecologic cancer clinical trials in the US between Jan.1994 and Nov.2021. Pre-specified return of value (ROV) items were abstracted from each protocol. Inclusion proportions were calculated for each ROV item and temporal changes assessed with chi-square tests. Temporality of proportional trends was further assessed by slope and departure from linearity calculations., Results: 279 gynecologic cancer clinical trials were included. Most commonly trials had first accrual in 2001-2007 (37%) and involved ovarian cancer (48%), phase II studies (53%), and chemotherapy (60%) or targeted therapy (34%). Trials often included ROV items in basic information (99%), medical record information (99%), and imaging (82%). 41% of trials included ROV items in biomarker testing, 20% genetic testing, and 20% in patient-reported outcome questionnaires. Over time, there were significant increases in the proportion of trials that included genetic (3% to 51%; p < 0.001) and biomarker testing (14 to 78%, p < 0.001). Information on lifestyle risk factors was rare (1%). No trials included ROV items in ancestry, how to connect with other participants, or remuneration., Conclusions: Gynecologic cancer clinical trials include few design elements that provide high value to Black individuals like lifestyle risk factors, ancestry, and remuneration. In any multi-pronged effort to improve diversity in clinical trial enrollment, inclusion of items valued by Black individuals should be considered., Competing Interests: Declaration of Competing Interest Ann Oluloro has received research support from a T32 grant (CA009515) and received a Scholar-In-Training award from the American Association for Cancer Research. Jonathan Jackson has received support from the National Institute on Aging (DP1 AG069873) and has moderated a discussion for Amgen. The other authors did not report any potential conflicts of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: This study aims to co-design an evidence- and theory-based behavioural intervention to reduce the impact of chemotherapy-induced peripheral neuropathy (CIPN) symptoms on patients' quality of life., Methods: Guided by the Medical Research Council Framework for developing and evaluating complex interventions, our intervention development process was guided by (a) findings of systematic reviews, (b) inductive analysis of 39 h of observational fieldwork, 12 patient and 11 clinician interviews, (c) deductive analysis using the Common-Sense Model to develop a Self-Regulation Model of CIPN and (d) 17 patients and 18 clinicians co-designing the intervention., Results: CIPN perception and coping behaviours were highlighted as processes to target when co-designing an intervention. The processes targeted in our intervention are CIPN perception and coping behaviours, namely, (a) self-monitoring of symptoms, (b) communicating and early reporting of symptoms to clinicians, (c) participating in making chemotherapy dose reduction decisions with their clinicians and (d) engaging in self-management and safety strategies to reduce impact of CIPN symptoms. To address these, a behavioural intervention was deemed suitable., Conclusion: We developed a self-regulation model of CIPN and a logic model for documenting the proposed mechanism of action of our co-designed behavioural intervention for reducing impact of CIPN symptoms., (© 2022 The Authors. European Journal of Cancer Care published by John Wiley & Sons Ltd.)

Background: Patient and Public Involvement is most usually framed in the context of designing, conducting and/or disseminating research. Participatory methods such as Experience-Based Co-Design (EBCD) further allow service users to directly engage in developing, testing and implementing interventions and services alongside healthcare staff. This paper aims to explore how participants in an EBCD project came-over time-to perceive their role and involvement in co-designing a cancer care intervention., Methods: The findings are based on our reflections, a research diary, email correspondence and fieldnotes from co-design events. Co-design participants who attended most of the ten co-design events took part through written reflections or audio-recorded video calls. Ten reflective pieces were collected from clinicians (n = 4), PPI group members/patient participants (n = 4), a doctoral researcher (n = 1) and a visual illustrator (n = 1). Inductive data analysis of participant reflections was carried out using reflexive thematic analysis. Meeting fieldnotes, email correspondence and the researcher's diary were deductively analysed using the initial themes generated from this inductive analysis., Results: Five main themes were identified: (1) changing perception of roles during the co-design process, (2) defining a 'co-designer', (3) engagement and ownership, (4) role of the research facilitator in maintaining momentum, and (5) perceived benefits of involvement., Conclusion: Our findings show the changing perceptions of roles and contributions among participants over time. Patients typically described their role as co-designers in terms simply of sharing their experiences. In contrast, clinicians perceived themselves as co-designers because they were working with patients who were actively involved in decision-making. Levels of engagement were affected by several factors such as time and facilitation, but most participants came to view themselves as co-owners of the intervention. Overall, participants perceived their involvement as a positive experience with clinicians also reporting wider positive impacts on their clinical practice., (© 2022. The Author(s).)