In species with separate sexes, the selection on males causes evolutionary change in female traits values (and vice versa) via genetic correlations, which has far-reaching consequences for adaptation. Here, we utilize a sex-specific form of Robertson's Secondary Theorem of Natural Selection to estimate the expected response to selection for 474 organismal-level traits and ~28,000 gene expression traits measured in the Drosophila Genetic Reference Panel (DGRP). Across organismal-level traits, selection acting on males produced a larger predicted evolutionary response than did selection acting on females, even for female traits; while for transcriptome traits selection on each sex produced a roughly equal average evolutionary response. For most traits, the selection on males and females was predicted to move average trait values in the same direction, though for some traits, the selection on one sex increased trait values while the selection on the other sex decreased them, implying intralocus sexual conflict. Our results provide support for the hypothesis that males experience stronger selection than females, potentially accelerating adaptation in females. Furthermore, sex-opposite responses to selection appear to exist for only a small proportion of traits, consistent with observations that the intersex genetic correlation for fitness is positive but less than one in most populations so far studied.

Insulin Resistance (IR) contributes to the pathogenesis of type 2 diabetes mellitus (T2DM) and is a risk factor for cardiovascular and neurodegenerative diseases. Amino acid and lipid metabolomic biomarkers associate with future T2DM risk in several epidemiological cohorts. Whether these biomarkers can accurately detect changes in IR status following treatment is unclear. Herein we evaluated the performance of clinical and metabolomic biomarker models to forecast altered IR, following lifestyle-based interventions. We evaluated the performance of two distinct insulin assay types (high-sensitivity ELISA and Immunoassay) and built IR diagnostic models using cross-sectional clinical and metabolomic data. These models were utilised to stratify IR status in pre-intervention fasting samples, from three independent cohorts (META-PREDICT (M-P, n=179), STRRIDE-AT/RT (S-2, n=116) and STRRIDE-PD (S-PD, n=149)). Linear and Bayesian projective prediction strategies were used to evaluate models for fasting insulin and HOMA2-IR and change in fasting insulin with treatment. Both insulin assays accurately quantified international standard insulin (R2>0.99), yet agreement for fasting insulin was less congruent (R2=0.65). A mean treatment effect on fasting insulin was only detectable using an ELISA. Clinical-metabolomic models were statistically related to fasting insulin (R2 0.33-0.39) but with modest capacity to classify IR at a clinically relevant HOMA2-IR threshold. Furthermore, no model predicted treatment responses in any cohort. We demonstrate that the choice of insulin assay is critical when quantifying the influence of treatment on fasting insulin, while none of the clinical-metabolomic biomarkers, identified in cross-sectional studies, are suitable for monitoring longitudinally changes in IR status.

Promoting social connection among older adults is a public health priority. Addressing hearing loss may reduce social isolation and loneliness among older adults. To describe the effect of a best-practice hearing intervention vs health education control on social isolation and loneliness over a 3-year period in the Aging and Cognitive Health Evaluation in Elders (ACHIEVE) study. This secondary analysis of a multicenter randomized controlled trial with 3-year follow-up was completed in 2022 and conducted at 4 field sites in the US (Forsyth County, North Carolina; Jackson, Mississippi; Minneapolis, Minnesota; Washington County, Maryland). Data were analyzed in 2024. Participants included 977 adults (aged 70-84 years who had untreated hearing loss without substantial cognitive impairment) recruited from the Atherosclerosis Risk in Communities study (238 [24.4%]) and newly recruited (de novo; 739 [75.6%]). Participants were randomized (1:1) to hearing intervention or health education control and followed up every 6 months. Hearing intervention (4 sessions with certified study audiologist, hearing aids, counseling, and education) and health education control (4 sessions with a certified health educator on chronic disease, disability prevention). Social isolation (Cohen Social Network Index score) and loneliness (UCLA Loneliness Scale score) were exploratory outcomes measured at baseline and at 6 months and 1, 2, and 3 years postintervention. The intervention effect was estimated using a 2-level linear mixed-effects model under the intention-to-treat principle. Among the 977 participants, the mean (SD) age was 76.3 (4.0) years; 523 (53.5%) were female, 112 (11.5%) were Black, 858 (87.8%) were White, and 521 (53.4%) had a Bachelor's degree or higher. The mean (SD) better-ear pure-tone average was 39.4 dB (6.9). Over 3 years, mean (SD) social network size reduced from 22.6 (11.1) to 21.3 (11.0) and 22.3 (10.2) to 19.8 (10.2) people over 2 weeks in the hearing intervention and health education control arms, respectively. In fully adjusted models, hearing intervention (vs health education control) reduced social isolation (social network size [difference, 1.05; 95% CI, 0.01-2.09], diversity [difference, 0.19; 95% CI, 0.02-0.36], embeddedness [difference, 0.27; 95% CI, 0.09-0.44], and reduced loneliness [difference, -0.94; 95% CI, -1.78 to -0.11]) over 3 years. Results were substantively unchanged in sensitivity analyses that incorporated models that were stratified by recruitment source, analyzed per protocol and complier average causal effect, or that varied covariate adjustment. This secondary analysis of a randomized clinical trial indicated that older adults with hearing loss retained 1 additional person in their social network relative to a health education control over 3 years. While statistically significant, it is unknown whether observed changes in social network are clinically meaningful, and loneliness measure changes do not represent clinically meaningful changes. Hearing intervention is a low-risk strategy that may help promote social connection among older adults. ClinicalTrials.gov Identifier: NCT03243422.

Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the underlying vascular cellular processes are chemo-mechanically driven, estimating the associated haemodynamic forces in vivo remains extremely difficult and uncertain. In this work, we propose a novel computational methodology for evaluating the blood flow dynamics across networks of myogenically-active cerebral arteries, which can modulate their muscular tone to stabilize flow (and perfusion pressure) as well as to limit vascular intramural stress. The introduced framework integrates a continuum mechanics-based, biologically-motivated model of the rat vascular wall with 1D blood flow dynamics. We investigate the time dependency of the vascular wall response to pressure changes at both single vessel and network levels. The dynamical performance of the vessel wall mechanics model was validated against different pressure protocols and conditions (control and absence of extracellular Ca2+). The robustness of the integrated fluid–structure interaction framework was assessed using different types of inlet signals and numerical settings in an idealized vascular network formed by a middle cerebral artery and its three generations. The proposed in-silico methodology aims to quantify how acute changes in upstream luminal pressure propagate and influence blood flow across a network of rat cerebral arteries. Weak coupling ensured accurate results with a lower computational cost for the vessel size and boundary conditions considered. To complete the analysis, we evaluated the effect of an upstream pressure surge on vascular network haemodynamics in the presence and absence of myogenic tone. This provided a clear quantitative picture of how pressure, flow and vascular constriction are re-distributed across each vessel generation upon inlet pressure changes. This work paves the way for future combined experimental-computational studies aiming to decipher cerebral autoregulation.

BackgroundIncreasingly, substance use and homelessness services have peer workers, those with lived or living experience of substance use and homelessness, who provide support to those experiencing similar challenges. While research regarding the effectiveness of such peer workers in helping others achieve better outcomes is growing, little is known about their experiences in this role.MethodsA systematic review and qualitative evidence synthesis was conducted to better understand the experiences of peer workers who have lived/living experience of substance use and homelessness who are providing support to those experiencing similar challenges within substance use and homelessness settings. Nine electronic databases were searched for primary qualitative research published from 1990. Studies meeting the inclusion criteria were quality assessed using the Critical Appraisal Skills Programme checklist. Data from included studies were extracted, entered into NVivo, and analysed using a thematic synthesis approach.ResultsNine studies were identified, published from 2006 from three countries with 272 participants. Three themes were identified: peer workers’ reflections on the key components of their role; peer work as enabling individual growth and recovery; and destabilising challenges peer worker growth and recovery.. Peer workers described many essential qualities, and their lived experience was valued as a way of enabling deeper trust and empathy with the people they supported. Strong relationships with other peer workers were described as important. Many benefits to the peer workers were described, including positive life changes and increased responsibility. Challenges were also identified, with professional boundaries causing particular tensions.ConclusionsThis qualitative evidence synthesis provides unique insight into the experiences of peer workers who are working at the intersection of homelessness and substance use. Their experiences highlight the real benefits that peer workers have, whilst working in challenging situations in often precarious contracts. Such insights can inform the employment of peer workers. Those employing peer workers should prioritise clear job descriptions encompassing specific peer qualities, training and education opportunities, and peer-to-peer, professional, and organisational support.

Abstract The correlations between Supermassive Black Holes (SMBHs) and their host galaxies still defy our understanding from both the observational and theoretical perspectives. Here we perform pairwise residual analysis on the latest sample of local inactive galaxies with a uniform calibration of their photometric properties and with dynamically measured masses of their central SMBHs. The residuals reveal that stellar velocity dispersion σ and, possibly host dark matter halo mass Mhalo, appear as the galactic properties most correlated with SMBH mass, with a secondary (weaker) correlation with spheroidal (bulge) mass, as also corroborated by additional Machine Learning tests. These findings may favour energetic/kinetic feedback from Active Galactic Nuclei (AGN) as the main driver in shaping SMBH scaling relations. Two state-of-the-art hydrodynamic simulations, inclusive of kinetic AGN feedback, are able to broadly capture the mean trends observed in the residuals, although they tend to either favour Msph as the most fundamental property, or generate too flat residuals. Increasing AGN feedback kinetic output does not improve the comparison with the data. In the Appendix we also show that the galaxies with dynamically measured SMBHs are biased high in σ at fixed luminosity with respect to the full sample of local galaxies, proving that this bias is not a byproduct of stellar mass discrepancies. Overall, our results suggest that probing the SMBH-galaxy scaling relations in terms of total stellar mass alone may induce biases, and that either current data sets are incomplete, and/or that more insightful modelling is required to fully reproduce observations.

ABSTRACT Software is designed for humans and must account for their values. However, current research and practice focus on a narrow range of well-explored values, e.g. security, overlooking a more comprehensive perspective. Those exploring a broader array of values rely on manual identification, which is labour-intensive and prone to human bias. Moreover, existing methods offer limited reliability as they fail to explain their findings. In this paper, we propose leveraging the reasoning capabilities of Large Language Models (LLMs) for automated inference about values. This allows for not only detecting values but also explaining how they are expressed in the software. We aim to examine the effectiveness of LLMs, specifically ChatGPT (Chat Generative Pre-Trained Transformer), in automated detection and explanation of values in software artifacts. Using ChatGPT, we investigate how mobile APIs align with human values based on their documentation. Human evaluation of ChatGPT's findings shows a reciprocal shift in understanding values, with both ChatGPT and experts adjusting their assessments through dialogue. While experts recognise ChatGPT's potential for revealing values, emphasis is placed on human involvement to enhance the accuracy of the findings by detecting and eliminating convincing but inaccurate explanations provided by the language model due to potential hallucinations or confabulations.