3DMOUSEneST: a volumetric, label-free imaging method evaluating embryo-uterine interaction and decidualization efficacy.

Effective interplay between the uterus and the embryo is essential for pregnancy establishment, however, convenient methods to screen embryo implantation success and maternal uterine response in experimental mouse models are currently lacking. Here we report 3DMOUSEneST, a groundbreaking method for analyzing mouse implantation sites based on label-free higher harmonic generation microscopy, providing unprecedented insights into the embryo-uterine dynamics during early pregnancy. The 3DMOUSEneST method incorporates second-harmonic generation microscopy to image the three-dimensional structure formed by decidual fibrillar collagen, named 'decidual nest', and third-harmonic generation microscopy to evaluate early conceptus (defined as the embryo and extraembryonic tissues) growth. We demonstrate that decidual nest volume is a measurable indicator of decidualization efficacy and correlates with the probability of early pregnancy progression based on a logistic regression analysis using Smad1/5 and Smad2/3 conditional knockout mice with known implantation defects. 3DMOUSEneST has great potential to become a principal method for studying decidual fibrillar collagen and characterizing mouse models associated with early embryonic lethality and fertility issues.

Significant shifts in latitudinal optima of North American birds.

Changes in climate can alter environmental conditions faster than most species can adapt. A prediction under a warming climate is that species will shift their distributions poleward through time. While many studies focus on range shifts, latitudinal shifts in species' optima can occur without detectable changes in their range. We quantified shifts in latitudinal optima for 209 North American bird species over the last 55 y. The latitudinal optimum (m) for each species in each year was estimated using a bespoke flexible non-linear zero-inflated model of abundance vs. latitude, and the annual shift in m through time was quantified. One-third (70) of the bird species showed a significant shift in their optimum. Overall, mean peak abundances of North American birds have shifted northward, on average, at a rate of 1.5 km per year (±0.58 SE), corresponding to a total distance moved of 82.5 km (±31.9 SE) over the last 55 y. Stronger poleward shifts at the continental scale were linked to key species' traits, including thermal optimum, habitat specialization, and territoriality. Shifts in the western region were larger and less variable than in the eastern region, and they were linked to species' thermal optimum, habitat density preference, and habitat specialization. Individual species' latitudinal shifts were most strongly linked to their estimated thermal optimum, clearly indicating a climate-driven response. Displacement of species from their historically optimal realized niches can have dramatic ecological consequences. Effective conservation must consider within-range abundance shifts. Areas currently deemed "optimal" are unlikely to remain so.

CoRegNet: unraveling gene co-regulation networks from public RNA-Seq repositories using a beta-binomial statistical model.

Millions of RNA sequencing samples have been deposited into public databases, providing a rich resource for biological research. These datasets encompass tens of thousands of experiments and offer comprehensive insights into human cellular regulation. However, a major challenge is how to integrate these experiments that acquired at different conditions. We propose a new statistical tool based on beta-binomial distributions that can construct robust gene co-regulation network (CoRegNet) across tens of thousands of experiments. Our analysis of over 12 000 experiments involving human tissues and cells shows that CoRegNet significantly outperforms existing gene co-expression-based methods. Although the majority of the genes are linearly co-regulated, we did discover an interesting set of genes that are non-linearly co-regulated; half of the time they change in the same direction and the other half they change in the opposite direction. Additionally, we identified a set of gene pairs that follows the Simpson's paradox. By utilizing public domain data, CoRegNet offers a powerful approach for identifying functionally related gene pairs, thereby revealing new biological insights.

Stochastic resonance in sparse neuronal network: functional role of ongoing activity to detect weak sensory input in awake auditory cortex of rat.

The awake cortex is characterized by a higher level of ongoing spontaneous activity, but it has a better detectability of weak sensory inputs than the anesthetized cortex. However, the computational mechanism underlying this paradoxical nature of awake neuronal activity remains to be elucidated. Here, we propose a hypothetical stochastic resonance, which improves the signal-to-noise ratio (SNR) of weak sensory inputs through nonlinear relations between ongoing spontaneous activities and sensory-evoked activities. Prestimulus and tone-evoked activities were investigated via in vivo extracellular recording with a dense microelectrode array covering the entire auditory cortex in rats in both awake and anesthetized states. We found that tone-evoked activities increased supralinearly with the prestimulus activity level in the awake state and that the SNR of weak stimulus representation was optimized at an intermediate level of prestimulus ongoing activity. Furthermore, the temporally intermittent firing pattern, but not the trial-by-trial reliability or the fluctuation of local field potential, was identified as a relevant factor for SNR improvement. Since ongoing activity differs among neurons, hypothetical stochastic resonance or "sparse network stochastic resonance" might offer beneficial SNR improvement at the single-neuron level, which is compatible with the sparse representation in the sensory cortex.

A comparative analysis of mutual information methods for pairwise relationship detection in metagenomic data.

BACKGROUND: Construction of co-occurrence networks in metagenomic data often employs correlation to infer pairwise relationships between microbes. However, biological systems are complex and often display qualities non-linear in nature. Therefore, the reliance on correlation alone may overlook important relationships and fail to capture the full breadth of intricacies presented in underlying interaction networks. It is of interest to incorporate metrics that are not only robust in detecting linear relationships, but non-linear ones as well. RESULTS: In this paper, we explore the use of various mutual information (MI) estimation approaches for quantifying pairwise relationships in biological data and compare their performances against two traditional measures-Pearson's correlation coefficient, r, and Spearman's rank correlation coefficient, ρ. Metrics are tested on both simulated data designed to mimic pairwise relationships that may be found in ecological systems and real data from a previous study on C. diff infection. The results demonstrate that, in the case of asymmetric relationships, mutual information estimators can provide better detection ability than Pearson's or Spearman's correlation coefficients. Specifically, we find that these estimators have elevated performances in the detection of exploitative relationships, demonstrating the potential benefit of including them in future metagenomic studies. CONCLUSIONS: Mutual information (MI) can uncover complex pairwise relationships in biological data that may be missed by traditional measures of association. The inclusion of such relationships when constructing co-occurrence networks can result in a more comprehensive analysis than the use of correlation alone.

Higher-order species interactions cause time-dependent niche and fitness differences: Experimental evidence in plant-feeding arthropods.

Species interact in different ways, including competition, facilitation and predation. These interactions can be non-linear or higher order and may depend on time or species densities. Although these higher-order interactions are virtually ubiquitous, they remain poorly understood, as they are challenging both theoretically and empirically. We propose to adapt niche and fitness differences from modern coexistence theory and apply them to species interactions over time. As such, they may not merely inform about coexistence, but provide a deeper understanding of how species interactions change. Here, we investigated how the exploitation of a biotic resource (plant) by phytophagous arthropods affects their interactions. We performed monoculture and competition experiments to fit a generalized additive mixed model to the empirical data, which allowed us to calculate niche and fitness differences. We found that species switch between different types of interactions over time, including intra- and interspecific facilitation, and strong and weak competition.

Present and future ofCosmoLattice.

We discuss the present state and planned updates ofCosmoLattice, a cutting-edge code for lattice simulations of non-linear dynamics of scalar-gauge field theories in an expanding background. We first review the current capabilities of the code, including the simulation of interacting singlet scalars and of Abelian and non-Abelian scalar-gauge theories. We also comment on new features recently implemented, such as the simulation of gravitational waves from scalar and gauge fields. Secondly, we discuss new extensions ofCosmoLatticethat we plan to release publicly. We comment on new physics modules, which include axion-gauge interactionsϕFF~, non-minimal gravitational couplingsϕ2R, creation and evolution of cosmic-defect networks, and magnetohydrodynamics. We also discuss new technical features, including evolvers for non-canonical interactions, arbitrary initial conditions, simulations in 2+1 dimensions, and higher-accuracy spatial derivatives.

Exchange-correlation effects in interatomic energies for pure density functionals and their application to the molecular energy prediction.

In this proof-of-concept paper, we show how exchange-correlation effects can be simply recovered for interatomic energies within the interacting quantum atoms decomposition when local, gradient generalized, or meta-gradient generalized approximations are used in density functional theory (DFT) calculations. We also demonstrate how inhomogeneity and non-local effects can be introduced even from a pure local scheme, without resorting to any orbital information. Finally, we provide numerical evidence on a database of selected energetic molecules that this decomposition scheme can be efficiently used to build accurate models for the prediction of molecular energies from an initial "cheap" DFT calculation.

Maternal pre-pregnancy body mass index and risk of preterm birth: a collaboration using large routine health datasets.

BACKGROUND: Preterm birth (PTB) is a leading cause of child morbidity and mortality. Evidence suggests an increased risk with both maternal underweight and obesity, with some studies suggesting underweight might be a greater factor in spontaneous PTB (SPTB) and that the relationship might vary by parity. Previous studies have largely explored established body mass index (BMI) categories. Our aim was to compare associations of maternal pre-pregnancy BMI with any PTB, SPTB and medically indicated PTB (MPTB) among nulliparous and parous women across populations with differing characteristics, and to identify the optimal BMI with lowest risk for these outcomes. METHODS: We used three UK datasets, two USA datasets and one each from South Australia, Norway and Denmark, together including just under 29 million pregnancies resulting in a live birth or stillbirth after 24 completed weeks gestation. Fractional polynomial multivariable logistic regression was used to examine the relationship of maternal BMI with any PTB, SPTB and MPTB, among nulliparous and parous women separately. The results were combined using a random effects meta-analysis. The estimated BMI at which risk was lowest was calculated via differentiation and a 95% confidence interval (CI) obtained using bootstrapping. RESULTS: We found non-linear associations between BMI and all three outcomes, across all datasets. The adjusted risk of any PTB and MPTB was elevated at both low and high BMIs, whereas the risk of SPTB was increased at lower levels of BMI but remained low or increased only slightly with higher BMI. In the meta-analysed data, the lowest risk of any PTB was at a BMI of 22.5 kg/m2 (95% CI 21.5, 23.5) among nulliparous women and 25.9 kg/m2 (95% CI 24.1, 31.7) among multiparous women, with values of 20.4 kg/m2 (20.0, 21.1) and 22.2 kg/m2 (21.1, 24.3), respectively, for MPTB; for SPTB, the risk remained roughly largely constant above a BMI of around 25-30 kg/m2 regardless of parity. CONCLUSIONS: Consistency of findings across different populations, despite differences between them in terms of the time period covered, the BMI distribution, missing data and control for key confounders, suggests that severe under- and overweight may play a role in PTB risk.

Link between triglyceride-glucose-body mass index and future stroke risk in middle-aged and elderly chinese: a nationwide prospective cohort study.

OBJECTIVE: Current literature is deficient in robust evidence delineating the correlation between the triglyceride glucose-body mass index (TyG-BMI) and the incidence of stroke. Consequently, this investigation seeks to elucidate the potential link between TyG-BMI and stroke risk in a cohort of middle-aged and senior Chinese individuals. METHODS: This study employs longitudinal data from four waves of the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2011, 2013, 2015, and 2018, encompassing 8,698 participants. The CHARLS cohort was assembled using a multistage probability sampling technique. Participants underwent comprehensive evaluations through standardized questionnaires administered via face-to-face interviews. Our analytic strategy involved the application of Cox proportional hazards regression models to investigate the association between TyG-BMI and the risk of stroke. To discern potential non-linear relationships, we incorporated Cox proportional hazards regression with smooth curve fitting. Additionally, we executed a battery of sensitivity and subgroup analyses to validate the robustness of our findings. RESULTS: Our study utilized a multivariate Cox proportional hazards regression model and found a significant correlation between the TyG-BMI and the risk of stroke. Specifically, a 10-unit increase in TyG-BMI corresponded to a 4.9% heightened risk of stroke (HR = 1.049, 95% CI 1.029-1.069). The analysis also uncovered a non-linear pattern in this relationship, pinpointed by an inflection point at a TyG-BMI value of 174.63. To the left of this inflection point-meaning at lower TyG-BMI values-a 10-unit hike in TyG-BMI was linked to a more substantial 14.4% rise in stroke risk (HR 1.144; 95% CI 1.044-1.253). Conversely, to the right of the inflection point-at higher TyG-BMI values-each 10-unit increment was associated with a smaller, 3.8% increase in the risk of stroke (HR 1.038; 95% CI 1.016-1.061). CONCLUSIONS: In the middle-aged and elderly Chinese population, elevated TyG-BMI was significantly and positively associated with stroke risk. In addition, there was also a specific non-linear association between TyG-BMI and stroke (inflection point 174.63). Further reduction of TyG-BMI below 174.63 through lifestyle changes and dietary control can significantly reduce the risk of stroke.

Cooperation in public goods game does not require assortment and depends on population density.

The threshold public goods game is one of the best-known models of non-linear public goods dilemmas. Cooperators and defectors typically coexist in this game when the population is assumed to follow the so-called structured deme model. In this article, we develop a dynamical model of a general N-player game in which there is no deme structure: Individuals interact with randomly chosen neighbours and selection occurs between randomly chosen pairs of individuals. We show that in the deterministic limit, the dynamics in this model leads to the same replicator dynamics as in the structured deme model, i.e., coexistence of cooperators and defectors is typical in threshold public goods game even when the population is completely well mixed. We extend the model to study the effect of density dependence and density fluctuation on the dynamics. We show analytically and numerically that decreasing population density increases the equilibrium frequency of cooperators till the fixation of this strategy, but below a critical density cooperators abruptly disappear from the population. Our numerical investigations show that weak density fluctuations enhance cooperation, while strong fluctuations suppress it.

High sensitivity stimulated Raman scattering microscopy with electronic resonance enhancement.

Raman microscopy is an important tool for labelfree microscopy. However, spontaneous Raman microscopy suffers from slow image acquisition rates and susceptibility to fluorescence background. Coherent Raman microsocopy techniques such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) microscopy, by contrast, offer fast imaging capability and robustness against sample fluorescence. Yet, their rather low sensitivity impedes their broader application. This review discusses sensitivity enhancement of SRS microscopy to µM detection levels by using electronically pre-resonant excitation. We present the foundations of this approach, discuss its technological implementation, and show first successful applications. A special emphasis is given to outlining new experimental developments allowing novel types of investigations.

The Role of cfDNA Biomarkers and Patient Data in the Early Prediction of Preeclampsia: Artificial Intelligence Model.

OBJECTIVE: Accurate individualized assessment of preeclampsia risk enables the identification of patients most likely to benefit from initiation of low-dose aspirin at 12-16 weeks' gestation when there is evidence for its effectiveness, as well as guiding appropriate pregnancy care pathways and surveillance. The primary objective of this study was to evaluate the performance of artificial neural network models for the prediction of preterm preeclampsia (<37 weeks' gestation) using patient characteristics available at the first antenatal visit and data from prenatal cell-free DNA (cfDNA) screening. Secondary outcomes were prediction of early onset preeclampsia (<34 weeks' gestation) and term preeclampsia (≥37 weeks' gestation). METHODS: This secondary analysis of a prospective, multicenter, observational prenatal cfDNA screening study (SMART) included singleton pregnancies with known pregnancy outcomes. Thirteen patient characteristics that are routinely collected at the first prenatal visit and two characteristics of cfDNA, total cfDNA and fetal fraction (FF), were used to develop predictive models for early-onset (<34 weeks), preterm (<37 weeks), and term (≥37 weeks) preeclampsia. For the models, the 'reference' classifier was a shallow logistic regression (LR) model. We also explored several feedforward (non-linear) neural network (NN) architectures with one or more hidden layers and compared their performance with the LR model. We selected a simple NN model built with one hidden layer and made up of 15 units. RESULTS: Of 17,520 participants included in the final analysis, 72 (0.4%) developed early onset, 251 (1.4%) preterm, and 420 (2.4%) term preeclampsia. Median gestational age at cfDNA measurement was 12.6 weeks and 2,155 (12.3%) had their cfDNA measurement at 16 weeks' gestation or greater. Preeclampsia was associated with higher total cfDNA (median 362.3 versus 339.0 copies/ml cfDNA; p<0.001) and lower FF (median 7.5% versus 9.4%; p<0.001). The expected, cross-validated area under the curve (AUC) scores for early onset, preterm, and term preeclampsia were 0.782, 0.801, and 0.712, respectively for the LR model, and 0.797, 0.800, and 0.713, respectively for the NN model. At a screen-positive rate of 15%, sensitivity for preterm preeclampsia was 58.4% (95% CI 0.569, 0.599) for the LR model and 59.3% (95% CI 0.578, 0.608) for the NN model.The contribution of both total cfDNA and FF to the prediction of term and preterm preeclampsia was negligible. For early-onset preeclampsia, removal of the total cfDNA and FF features from the NN model was associated with a 6.9% decrease in sensitivity at a 15% screen positive rate, from 54.9% (95% CI 52.9-56.9) to 48.0% (95% CI 45.0-51.0). CONCLUSION: Routinely available patient characteristics and cfDNA markers can be used to predict preeclampsia with performance comparable to other patient characteristic models for the prediction of preterm preeclampsia. Both LR and NN models showed similar performance.

Non-linear relationships in clinical research.

True linear relationships are rare in clinical data. Despite this, linearity is often assumed during analyses, leading to potentially biased estimates and inaccurate conclusions. In this introductory paper, we aim to first describe - in a non-mathematical manner - how to identify non-linear relationships. Various methods are then discussed that can be applied to deal with non-linearity, including transformations, polynomials, splines, and Generalized Additive Models (GAMs), along with their strengths and weaknesses. Finally, we illustrate the use of these methods with a practical example from nephrology, providing guidance on how to report the results from non-linear relationships.

Model-based bioequivalence approach for sparse pharmacokinetic bioequivalence studies: Model selection or model averaging?

Conventional pharmacokinetic (PK) bioequivalence (BE) studies aim to compare the rate and extent of drug absorption from a test (T) and reference (R) product using non-compartmental analysis (NCA) and the two one-sided test (TOST). Recently published regulatory guidance recommends alternative model-based (MB) approaches for BE assessment when NCA is challenging, as for long-acting injectables and products which require sparse PK sampling. However, our previous research on MB-TOST approaches showed that model misspecification can lead to inflated type I error. The objective of this research was to compare the performance of model selection (MS) on R product arm data and model averaging (MA) from a pool of candidate structural PK models in MBBE studies with sparse sampling. Our simulation study was inspired by a real case BE study using a two-way crossover design. PK data were simulated using three structural models under the null hypothesis and one model under the alternative hypothesis. MB-TOST was applied either using each of the five candidate models or following MS and MA with or without the simulated model in the pool. Assuming T and R have the same PK model, our simulation shows that following MS and MA, MB-TOST controls type I error rates at or below 0.05 and attains similar or even higher power than when using the simulated model. Thus, we propose to use MS prior to MB-TOST for BE studies with sparse PK sampling and to consider MA when candidate models have similar Akaike information criterion.

Evaluation of adherence to abiraterone therapy in prostate cancer patients based on a population pharmacokinetic model.

AIMS: Abiraterone treatment requires regular drug intake under fasting conditions due to pronounced food effect, which may impact patient adherence. The aim of this prospective study was to evaluate adherence to abiraterone treatment in patients with prostate cancer. To achieve this aim, an abiraterone population pharmacokinetic model was developed and patients' adherence has been estimated by comparison of measured levels of abiraterone with population model-based simulations. METHODS: A total of 1469 abiraterone plasma levels from 83 healthy volunteers collected in a bioequivalence study were analysed using a nonlinear mixed-effects model. Monte Carlo simulation was used to describe the theoretical distribution of abiraterone pharmacokinetic profiles at a dose of 1000 mg once daily. Adherence of 36 prostate cancer patients treated with abiraterone was then evaluated by comparing the real abiraterone concentration measured in each patient during follow-up visit with the theoretical distribution of profiles based on simulations. Patients whose abiraterone levels were ˂5th or ˃95th percentile of the distribution of simulated profiles were considered to be non-adherent. RESULTS: Based on this evaluation, 13 patients (36%) have been classified as non-adherent. We observed significant association (P = .0361) between richness of the breakfast and rate of non-adherence. Adherent patients reported significantly better overall condition in self-assessments (P = .0384). A trend towards a higher occurrence of adverse effects in non-adherent patients was observed. CONCLUSIONS: We developed an abiraterone population pharmacokinetic model and proposed an advanced approach to medical adherence evaluation. Due to the need for administration under fasting conditions, abiraterone therapy is associated with a relatively high rate of non-adherence.

Linear and non-linear relationships between body fat mass distribution and bone mineral density in adults: The NHANES, 2011-2018.

OBJECTIVE: The relationship between body fat mass and bone mineral density (BMD) remains controversial. This research aimed to explore the linear or non-linear relationship between body fat mass and BMD among adults in the United States. METHODS: This cross-sectional study identified adults aged 18 years or older in the National Health and Nutrition Examination Survey from 2011 to 2018. After adjusting for covariates, linear relationships between body fat mass and BMD in different genders were tested by generalized linear models, and potential non-linear relationships were explored by generalized additive models and piecewise linear regression models. RESULTS: The research included 4691 (57.9% of the total sample) males and 3417 (42.1% the of total sample) females. In both males and females, we found a negative association between android or total body fat mass and lumbar spine BMD and a positive association between appendicular, android, gynoid, or total body fat mass and whole body BMD (all P < 0.05). The relationships between body fat mass in all regions and lumbar spine BMD were U-shaped in males and inverted U-shaped in females (all Pnon-linear < 0.05). Inverted U-shaped relationships existed between body fat mass in all regions and whole body BMD in females (all Pnon-linear < 0.05). CONCLUSIONS: Body fat mass was negatively and linearly associated with lumbar spine BMD, but positively associated with whole body BMD. Body fat mass had a U-shaped relationship with lumbar spine BMD in males and an inverted U-shaped association with lumbar spine and whole body BMD in females.

Non-linear relationship between the body roundness index and metabolic syndrome: data from National Health and Nutrition Examination Survey (NHANES) 1999-2018.

Obesity is an important characteristic manifestation of metabolic syndrome (MetS), and body roundness index (BRI) is one of the anthropometric indicators associated with obesity. However, studies on the relationship between BRI and MetS risk are limited. We aimed to explore the relationship between baseline BRI and MetS in the USA population. Our study used data from the National Health and Nutrition Examination Survey from 1999 to 2018, ultimately enrolling and analysing 47 303 participants. Data-driven tertiles were used to categorise BRI levels, and multivariate logistic regression models were fitted to investigate the association of BRI with MetS in adults. In addition, receiver operating characteristic curve analysis was used to assess the ability of BRI to predict MetS. The distribution of BRI was different across ethnic groups with a gradual decrease in the proportion of non-Hispanic Whites and other races. In addition, BRI was significantly associated with traditional cardiovascular risk factors. Univariate regression analysis indicated BRI to be a moderate risk factor for MetS, and multivariate logistic regression analysis found that BRI remained an independent risk factor for MetS. After adjusting for confounding variables, a non-linear relationship was found between BRI levels and the prevalence of MetS. More importantly, BRI predicted MetS with the largest AUC among anthropometric measures. In summary, elevated baseline BRI levels are independently associated with the development of MetS, and baseline BRI may assist in identifying patients at risk for MetS, leading to early and optimal treatment to improve their outcomes.

Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol.

Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the "residual" exposure). These "local" causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method-the doubly-ranked method-has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects-that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed.

The optimal design of assisted reproductive technologies policies.

This paper studies the optimal fiscal treatment of assisted reproductive technologies (ART) in an economy where individuals differ in their reproductive capacity (or fecundity) and in their wage. We find that the optimal ART tax policy varies with the postulated social welfare criterion. Utilitarianism redistributes only between individuals with unequal fecundity and wages but not between parents and childless individuals. To the opposite, ex post egalitarianism (which gives absolute priority to the worst-off in realized terms) redistributes from individuals with children toward those without children, and from individuals with high fecundity toward those with low fecundity, so as to compensate for both the monetary cost of ART and the disutility from involuntary childlessness resulting from unsuccessful ART investments. Under asymmetric information and in order to solve for the incentive problem, utilitarianism recommends to either tax or subsidize ART investments of low-fecundity-low-productivity individuals at the margin, depending on the degree of complementarity between fecundity and ART in the fertility technology. On the opposite, ex post egalitarianism always recommends marginal taxation of ART.