Myopic Optimization with Non-myopic Approval (MONA) mitigates multi-step reward hacking by restricting the agent's planning horizon while supplying far-sighted approval as a training signal~\cite{farquhar2025mona}. The original paper identifies a critical open question: how the method of constructing approval -- particularly the degree to which approval depends on achieved outcomes -- affects whether MONA's safety guarantees hold. We present a reproduction-first extension of the public MONA Camera Dropbox environment that (i)~repackages the released codebase as a standard Python project with scripted PPO training, (ii)~confirms the published contrast between ordinary RL (91.5\% reward-hacking rate) and oracle MONA (0.0\% hacking rate) using the released reference arrays, and (iii)~introduces a modular learned-approval suite spanning oracle, noisy, misspecified, learned, and calibrated approval mechanisms. In reduced-budget pilot sweeps across approval methods, horizons, dataset sizes, and calibration strategies, the best calibrated learned-overseer run achieves zero observed reward hacking but substantially lower intended-behavior rates than oracle MONA (11.9\% vs.\ 99.9\%), consistent with under-optimization rather than re-emergent hacking. These results operationalize the MONA paper's approval-spectrum conjecture as a runnable experimental object and suggest that the central engineering challenge shifts from proving MONA's concept to building learned approval models that preserve sufficient foresight without reopening reward-hacking channels. Code, configurations, and reproduction commands are publicly available. https://github.com/codernate92/mona-camera-dropbox-repro
Reproductive well-being education remains widely stigmatized across diverse cultural contexts, constraining how individuals access and interpret reproductive health knowledge. We designed and evaluated OpenBloom, a stigma-sensitive, AI-mediated system that uses LLMs to transform reproductive health articles into reflective, question-based learning prompts. We employed OpenBloom as a design probe, aiming to explore the emerging challenges of reproductive well-being stigma through LLMs. Through surveys, semi-structured interviews, and focus group discussions, we examine how sociocultural stigma shapes participants' engagements with AI-generated questions and the opportunities of inquiry-based reproductive health education. Our findings identify key design considerations for stigma-sensitive LLM, including empathetic framing, inclusive language, values-based reflection, and explicit representation of marginalized identities. However, while current LLM outputs largely meet expectations for cultural sensitivity and non-offensiveness, they default to superficial rephrasing and factual recall rather than critical reflection. This guides well-being HCI design in sensitive health domains toward culturally grounded, participatory workflows.
Zheng-Hui Zhao, Xue-Ying Chen, Cheng-Yan Zhuo
et al.
Diabetes is associated with compromised reproductive health; however, the cellular and molecular mechanisms underlying its impact on ovarian function remain largely unclear. In this study, we integrated single-cell RNA sequencing, DNA methylation profiling, and metabolomic analyses to comprehensively characterize the ovarian cellular landscape, epigenetic alterations, and metabolic reprogramming in diabetic female mice, with a focus on identifying diabetes-induced changes in ovarian cells. Our cell type-specific transcriptomic analysis revealed that dysregulated steroid hormone biosynthesis and impaired fatty acid metabolism are prominent features of diabetic ovarian dysfunction. Notably, key genes including Cyp11a1, Fshr, and Lhcgr exhibited reduced expression accompanied by increased DNA methylation levels in their gene regions within granulosa cells under diabetic conditions. Furthermore, disrupted granulosa cell differentiation was evident, leading to aberrant luteal cell formation and compromised luteal function. In parallel, metabolomic profiling revealed profound metabolic reprogramming in diabetic ovaries, with significant alterations in lipid metabolism pathways, including elevated unsaturated fatty acid and reduced glycerophospholipid metabolism. Taken together, these findings provide novel insights into the molecular pathways underlying ovarian dysfunction in the context of diabetes, thereby enhancing our understanding of folliculogenesis in metabolic disorders.
Ogechi Regina Amanso, Jeconia Okelo Abonyo, Phineas Roy Kiogora
et al.
Human papillomavirus (HPV) is a global health problem that causes the vast majority of cervical cancers. { A novel mathematical model for HPV, as it progresses to cervical cancer, was formulated using a system of six ordinary differential equations that incorporates behavioural dynamics in the description of some control measures. In particular, this study introduces a novel chained dependency framework where the awareness parameter depends on Cancer burden, and the screening rate is a function of awareness}. The essential epidemiological features of the model, such as the positivity and boundedness of the model, the basic reproduction number $\mathcal{R}_0$, the disease-free equilibrium, and the endemic equilibrium, are derived. The disease-free equilibrium is shown to be locally and globally asymptotically stable when $\mathcal{R}_0 < 1$. The endemic level of infection is expressed in terms of $\mathcal{R}_0$, and deductions are made from their relationship. Sensitivity analysis is conducted to determine which parameters are of utmost importance using a global sensitivity analysis method called the Partial Rank Correlation Coefficient (PRCC) method. Parameters are set from the literature, and simulation is carried out using the Runge--Kutta 4th order (RK4) method to explore the impact of various parameters on model dynamics. { The results of the analyses not only reveal the impact of awareness campaigns, routine screening programmes, and vaccination in reducing HPV and cervical cancer, but also demonstrate how improved disease outcomes are directly linked to the chained awareness screening structure rather than the usual epidemic dynamics.
Mobile application performance is a vital factor for user experience. Yet, performance issues are notoriously difficult to detect in development environments, where they often manifest less conspicuously, making their diagnosis more challenging. In this setting, app reviews from users with diverse device configurations can provide timely and context-rich information about emerging performance issues. However, unlike structured bug reports, app reviews are written by end-users and tend to be more ambiguous, with individual reviews often providing only partial descriptions of the underlying issue. To bridge this gap, we present RevPerf, the first approach to automatically reproduce mobile application performance issues by leveraging and synthesizing information from app reviews. RevPerf retrieves complementary reviews via semantic retrieval and uses prompt engineering to integrate them, enriching the original review with performance issue details. An execution agent is then employed to generate and execute commands to reproduce the issue. After executing all necessary steps, the system incorporates multifaceted detection methods to identify performance issues by monitoring Android logs, GUI changes, and system resource utilization during the reproduction process. Experimental results demonstrate that our proposed framework achieves a 72.73% success rate in reproducing performance issues on the constructed dataset, outperforming the best baseline by 27.28%.
Abstract This study investigated the relationship between dyslipidemia prior to conception and the risk of preeclampsia (PE) in women pregnant by in vitro fertilization and embryo transfer (IVF-ET). The retrospective cohort study consisted of 2994 women who conceived by IVF-ET and delivered live neonates. The study population was divided into two components: a training set for the prediction model development (2288 women) and a test set for validation (706 women). Multivariable logistic regression was used for the development and validation of predictive model for the risk of PE. Among the 2288 women in the training set, 266 women (11.6%) developed PE. Multiple logistic regression analysis identified independent predictors for PE: triglyceride (TG) [adjusted odds ratio (aOR) 1.284; 95% confidence interval (CI) 1.113–1.489, P < 0.001]; pre-pregnancy BMI; pre- chronic hypertension; twin pregnancy; embryo transfer protocol. These independent predictors for PE were used to form a risk prediction model, and the area under the receiver-operator characteristic (ROC) curve (AUC) in the training and the test set was 0.77 (95% CI 0.73–0.80)and 0.71 AUC (95% CI 0.65–0.77), respectively. In conclusion, higher TG levels before pregnancy were independently associated with the risk for PE in women pregnant by IVF-ET.
ABSTRACT Resource availability should have consequences for life‐history functions and trade‐offs among them because it influences the amounts of resources allocated to different functions. Nutritional status during a key developmental window (sexual maturation) may also have an important impact on life‐history functions and such trade‐offs. However, less is known about whether and how they interact to influence the resource allocation of individuals. Here, we simultaneously manipulated female nutritional status during sexual maturation and resource availability during breeding in a burying beetle Nicrophorus vespilloides. We then monitored the main and interactive effects of these two factors on somatic maintenance and reproductive performance of burying beetle females. We found that variation in nutritional status during sexual maturation affects the resource allocation of burying beetle females only at the pre‐hatching stage. Poor‐fed females compensated for the initial differences in energy reserves by feeding from the carcass or engaged in terminal investment strategy and invested heavily at the post‐hatching stage. Specifically, poor‐fed females allocated more into somatic maintenance (gained more weight) and less into reproduction (provided less pre‐hatching care) than well‐fed females, whereas they provided a similar amount and duration of post‐hatching care. In addition, burying beetles with different nutritional statuses vary in their response to resource availability. Poor‐fed females allocated more into both somatic maintenance (gained more weight) and reproduction (provided more pre‐hatching care) when bred on large versus small carcasses, whereas well‐fed females tend to work near their maximum capacity and thus show no response to resource availability. Finally, our findings suggest that poor‐fed females did not suffer a future cost in offspring performance. Meanwhile, a large carcass allowed females to produce more and heavier offspring. These findings enhance our understanding of how important nutritional status during a key developmental window and resource availability during breeding is for the expression of resource allocation.
Environmental factors, particularly various components of fine particulate matter (PM2.5) (i.e., sulfate [SO42-], nitrate [NO3-], ammonium [NH4+], organic matter [OM] and black carbon [BC]), are increasingly recognized as potential risk factors for poor ovarian response (POR) in fertility treatments. However, existing research is limited, and the critical periods of vulnerability to exposure to PM2.5 and its components remain unclear. In this retrospective cohort study, we included 38,619 patients undergoing their first in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) treatment, defining POR as the primary outcome based on the POSEIDON criteria. We divided the six months prior to oocyte pick up (OPU) into different exposure windows and used logistic regression models to assess the association between pollutants and POR. Results showed that exposure to PM2.5 and its components in the three months before OPU significantly increased the odds of POR. The distributed lag nonlinear model (DLNM) analysis revealed the lagged effects of PM2.5 component exposure, particularly during lag weeks 2–5, where exposure was significantly associated with the occurrence of POR. This period may represent a sensitive window for exposure. Meanwhile, the restricted cubic spline (RCS) analysis indicated that the odds of POR gradually increased with higher pollutant concentrations. These findings underscore the urgent need for public health measures during sensitive stages of follicular development, particularly policies aimed at reducing environmental pollutant exposure among women of reproductive age to protect reproductive health.
Luigia Bosa, Simona Mattioli, Anna Maria Stabile
et al.
The aim of this study was to analyze how recombinant rabbit NGF (Nerve Growth Factor) encapsulated in chitosan (rrβNGFch) affects sperm viability, motility, capacitation, acrosome reaction (AR), kinetic traits, and apoptosis after 30 min and 2 h of storage. Specific intracellular signaling pathways associated with either cell survival, such as protein kinase B (AKT) and extracellular signal-regulated kinases 1/2 (ERK1/2), or programmed cell death, such as c-Jun N-terminal kinase (JNK), were also analyzed. The results confirmed the effect of rrβNGFch on capacitation and AR, whereas a longer storage time (2 h) decreased all qualitative sperm traits. AKT and JNK did not show treatment-dependent activation and lacked a correlation with functional traits, as shown by ERK1/2. These findings suggest that rrβNGFch may promote the functional activation of sperm cells, particularly during early incubation. The increase in capacitation and AR was not linked to significant changes in pathways related to cell survival or death, indicating a specific action of the treatment. In contrast, prolonged storage negatively affected all sperm parameters. ERK1/2 activation correlated with capacitation, AR, and apoptosis, supporting its role as an NGF downstream mediator. Further studies should analyze other molecular mechanisms of sperm and the potential applications of NGF in assisted reproduction.
Fabio I. Martinenghi, Xian Zhang, Luk Rombauts
et al.
A growing share of the world's population is being born via assisted reproductive technology (ART), including in-vitro fertilisation (IVF). However, two concerns persist. First, ART pregnancies correlate with predictors of poor outcomes at birth--and it is unclear whether this relationship is causal. Second, the emotional and financial costs associated with ART-use might exacerbate defensive medical behaviour, where physicians intervene more than necessary to reduce the risk of adverse medical outcomes and litigation. We address the challenge of identifying the pure effect of ART-use on both maternal and infant outcomes at birth by leveraging exogenous variation in the success of ART cycles. We compare the obstetric outcomes for ART-conceived births with those of spontaneously-conceived births after a failed ART treatment. Moreover, we flexibly adjust for key confounders using double machine learning. We do this using clinical registry ART data and administrative maternal and infant data from New South Wales (NSW) between 2009-2017. We find that ART slightly decreases the risk of obstetric interventions, lowering the risk of a caesarean section and increasing the rate of spontaneous labour (+3.5 p.p.). Moreover, we find that ART has a statistically and clinically insignificant effect on infant health outcomes. Keywords: Fertility, Assisted reproduction, IVF, Caesarean Section, Obstetric, Infertility. JEL classification: I10, I12, I19.
In software development, bug report reproduction is a challenging task. This paper introduces ReBL, a novel feedback-driven approach that leverages GPT-4, a large-scale language model (LLM), to automatically reproduce Android bug reports. Unlike traditional methods, ReBL bypasses the use of Step to Reproduce (S2R) entities. Instead, it leverages the entire textual bug report and employs innovative prompts to enhance GPT's contextual reasoning. This approach is more flexible and context-aware than the traditional step-by-step entity matching approach, resulting in improved accuracy and effectiveness. In addition to handling crash reports, ReBL has the capability of handling non-crash functional bug reports. Our evaluation of 96 Android bug reports (73 crash and 23 non-crash) demonstrates that ReBL successfully reproduced 90.63% of these reports, averaging only 74.98 seconds per bug report. Additionally, ReBL outperformed three existing tools in both success rate and speed.
After the repeal of Roe vs. Wade in June 2022, women face long-distance travel across state lines to access abortion care. For women who also face socioeconomic hardship, travel for abortion care is a significant burden. To ease this burden, abortion access nonprofits are funding and/or supplying transportation to abortion clinics. However, due to the uneven distribution of demand and supply for abortions, these nonprofits do not have efficient logistical operations. As a result, low-income, underserved women may not have access to adequate reproductive healthcare, thus widening healthcare inequity gaps. Nonprofits may also risk not serving the needs of vulnerable women without access to adequate reproductive healthcare, and in doing so, waste resources, money, and volunteer hours. To address these challenges, we create an interactive, web-based planning tool, the Reproductive Healthcare Equity Algorithm (RHEA), to guide nonprofits in strategically allocating resources and serving demand. RHEA leverages an optimization model to determine the maximum flow and minimum transportation cost to route women across a network of counties and abortion clinics, subject to transportation supply, budget, and time constraints for one day of operations for a nonprofit. In doing so, we collaborate with abortion access nonprofits to cater our model design and interface development to their needs and considerations. Ultimately, we seek to optimize resource allocation for nonprofits providing abortion care logistics and improve abortion access for low-income, underserved women.
Elodie Germani, Nikhil Baghwat, Mathieu Dugré
et al.
Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to reproduce (re-implementing the experiments with the same data, same method) and replicate (different data and/or method) the models described in [1] to predict individual's PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson's Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in [1] and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction and compare the reproduced results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 \> 0), which is consistent with its findings. Moreover, using derived data provided by the authors of the original study, we were able to make an exact reproduction and managed to obtain results that were close to the original ones. The challenges encountered while reproducing and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.
Aglaja Busch, Lorena R. R. Gianotti, Frank Mayer
et al.
# Background
Changes in cortical activation patterns after rupture of the anterior cruciate ligament (ACL) have been described. However, evidence of these consequences in the early stages following the incident and through longitudinal monitoring is scarce. Further insights could prove valuable in informing evidence-based rehabilitation practices.
# Purpose
To analyze the angular accuracy, neuromuscular, and cortical activity during a knee joint position sense (JPS) test over the initial six months following ACL reconstruction.
Study design: Cohort Study
# Methods
Twenty participants with ACL reconstruction performed a JPS test with both limbs. The measurement time points were approximately 1.5, 3-4 and 6 months after surgery, while 20 healthy controls were examined on a single occasion. The active JPS test was performed seated with a target angle of 50° for two blocks of continuous angular reproduction (three minutes per block). The reproduced angles were recorded simultaneously by an electrogoniometer. Neuromuscular activity of the quadriceps muscles during extension to the target angle was measured with surface electromyography. Spectral power for theta, alpha-2, beta-1 and beta-2 frequency bands were determined from electroencephalographic recordings. Linear mixed models were performed with group (ACL or controls), the measurement time point, and respective limb as fixed effect and each grouping per subject combination as random effect with random intercept.
# Results
Significantly higher beta-2 power over the frontal region of interest was observed at the first measurement time point in the non-involved limb of the ACL group in comparison to the control group (p = 0.03). Despite individual variation, no other statistically significant differences were identified for JPS error, neuromuscular, or other cortical activity.
# Conclusion
Variation in cortical activity between the ACL and control group were present, which is consistent with published results in later stages of rehabilitation. Both indicate the importance of a neuromuscular and neurocognitive focus in the rehabilitation.
# Level of Evidence
3