This statistic shows the results of a survey among working female fashion models based in the United States on how old they were when they first started working in the fashion industry. 54.7 percent of respondents stated they were between 13 and 16 years old when they started working as a model.

Age estimates are based on mean estimate from raters. Identifiability scores (%) are based on the mean number of times that the model’s posed facial expressions were correctly identified by the raters.

In 2016, the average size of a model was of **** meters while French women were on average **** meters tall. The fashion and beauty industry appear to have set physical and appearance standards which do not correspond to the reality of the bodies of most of women all over the world. Thus, in a survey conducted by Harris Interactive in 2018, ** percent of people in France admitted to have physical complexes.

Fashion industry vs reality

According to the source, which shows a comparison of body measurements for the average French women and female model, models are taller and slimmer than the average French woman. For instance, the average hip circumference of a French woman reached 100 centimeters, compared to ** centimeters for a female model. With height excluding, French women had also more centimeters when it comes to chest size or waist circumference. Unrealistic body images have an impact on the way individuals, and particularly younger generations, perceived their appearance and their body. In a study from 2009/2010, 37 percent of French girls aged 13 years old declared they thought they were too fat. This number even reached ** percent among German female teenagers the same age. The belly, as well as the hips, are often the least loved body parts of French people because of being considered not thin enough

Body care in France

Body care, and mostly face care, is very common in France. Most French consider that the face is the body part they take most care of. The country is known for its drugstores (also called parapharmaceutical shops), which represented ** percent of the beauty and hygiene market value in 2014. Yves Rocher products, as well as Diadermine or Avène, are among the preferred skin care products of French women. In 2018, the total sales revenue of hygiene products in France amounted to more than **** billion euros.

This paper examines the age at retirement for men and women in Italy. Despite the expansion of women’s educational attainments, they still display lower employment rates, are frequently engaged in involuntary part-time jobs and have more fragmented careers. As a consequence, the mean age at which women receive a pension is higher than that of men. Using Labour Force Survey (2006 and 2012), the authors test the hypothesis that women’s higher age at retirement is determined by a selection bias towards more educated and work oriented women. A Heckman selection model has been developed. Results show that the main disadvantage is suffered by women with medium and low levels of education who show the highest estimated age at retirement, whereas higher educated women retire on average before men with the same level of education. The authors argue that pension policies, without interventions in the field of work-life balance policies, end up penalizing women with lower levels of education.

While it is commonly thought that patrilocality is associated with worse outcomes for women 27 and their children due to lower social support, few studies have examined whether the structure 28 of female social networks covaries with post-marital residence. Here we analyze scan sample 29 data collected among Tsimane forager-farmers. We compare the social groups and activity 30 partners of 181 women residing in the same community as their parents, their husband’s parents, 31 both or neither. Relative to women living closer to their in-laws, women living closer to their 32 parents are less likely to be alone or solely in the company of their nuclear family (OR: 0.6, 33 95%CI: 0.3-0.9), and more likely to be observed with others when engaging in food processing 34 and manufacturing of market or household goods, but not other activities. Women are slightly 35 more likely to receive childcare support from outside the nuclear family when they live closer to 36 their parents (OR=1.8, 95%CI 0.8 - 3.9). Their social group size and their children’s probability 37 of receiving allocate decrease significantly with distance from their parents, but not their in-laws. 38 Our findings highlight the importance of women’s proximity to kin, but also indicate that 39 patrilocality per se is not costly to Tsimane women. Methods Data collection took place between March 2002 and November 2007 in 9 separate Tsimane communities. In each community, households were sorted into clusters of multiple physically close houses from within which researchers could easily monitor the activity of all inhabitants. Clusters were then selected for data collection at random without replacement until all clusters were sampled. Data collection involved monitoring each member of the cluster households for 2-to-3-hour blocks between 7AM and 7PM, with point scans every half hour. During point scans, the location, activity, and objects of interaction of each individual was recorded. Individuals were coded as being in the same social group if they were either a) engaged in active conversation or b) within 3 meters of each other, and in the same activity group if they were engaged in the same activity. When household members from the sampled cluster were absent, their whereabouts, activity, and (where possible) companions were ascertained by asking their family members. For this analysis, we selected scans of mothers of children under the age of 14, excluding visitors to the communities. This resulted in a total sample of 11940 observations of 181 Tsimane women, ranging in age between 15 and 59 with an average age of 32 (see table 1). For each woman’s scan we examined the list of individuals aged 14 or over who were in a) her social group or b) her activity group during the scan, excluding her husband and children. From this list we then calculated the total number of individuals engaged in: (1) Any activity; (2) Hunting, fishing, or gathering food; (3) Manufacturing cloth, bags, or jatata thatch; (4) Garden labor or wage labor, and (5) Processing or preparing food. Next, we selected observations of the children under the age of seven, which corresponds to the age range when Tsimane children require most supervision. This amounted to a sample of 21,938 observations of 351 children (52% male). Children were coded as receiving extra-familial childcare either if they were recorded receiving direct care (e.g., holding, playing, feeding, teaching, etc.) or if they were engaged in some social interaction with an individual 11 years or older, at which age we determined any social interaction with a child under seven could be reasonably construed as childcare based on ethnographic insights and existing literature in traditional societies [48,49]. Siblings were excluded as providers of childcare in this analysis since their presence is not tied to the post-marital residence choices of their parents. Because Tsimane adults often supervise children passively rather than actively caring for them, we also tested whether residence patterns affected children’s probability of being unsupervised, which we coded as being in a social group with no adults. The post-marital residence choices of the women in our sample were coded in two ways. First, we categorized the women as being either patrilocal, matrilocal, bilocal, or neolocal based on the known residences of their parents and parents-in-law, following Gruitjers and Ermisch [50] . Couples for whom no information existed for either set of parents were assigned according to the presence of siblings in their home community. Accordingly, women coded as neolocal lived in communities where none of their or their husbands’ nuclear family lived. Bilocal families had at least 1 parent of each of the husband and the wife living in the same community. As a robustness check we also analyzed a sub-sample of families for whom GPS data existed for at least one parent of both the husband and the wife. Starting in 2007, the THLHP and its collaborators have collected GPS data for every household, which we used to reconstruct a subsample of the households where data was collected. When the precise GPS location was unavailable, but the community was known, which generally occurred when the parent or in-law lived in a non-sample community, we took their location to be the central point of their community, which given the distances between communities is a fairly accurate estimate on the log scale. This sub-sample included 83 women and 180 children, which corresponds to ~50% of the total sample. Using these data, we were able to model women’s social group size and children’s probability of receiving allocare as a function of the (ln-transformed) distance from the woman’s parents (the child’s maternal grandparents) and her in-laws (the paternal grandparents). All analyses were conducted in R version 4.1.2. We fit generalized linear multilevel models (GLMMs) using the glmmTMB package, which allows for mixed-effect hurdle and zero-inflation models. To account for the possible overdispersion of the count data, specifically the observed size of women’s social and activity groups, we compared multilevel Poisson, negative binomial, and zero-inflated Poisson models, all adjusting for mothers’ age, age squared, and the time of day of the observation block (morning or afternoon), with random intercept terms to control for repeated observations of individuals as well as the communities. Mother’s age was selected because of its possible causal influence over both residence and social group size. Including age squared significantly improved model fit according to likelihood ratio tests (Chi-square=9.06, p= 0.011). Time of day also had a significant effect on group size in many models, and due to sampling randomness may have varied across residence patterns, so was included in the model as a control. Likelihood ratio tests confirmed that the zero-inflated models were much better fit to the data than Poisson and negative binomial models (Electronic Supplementary Materials [ESM], table S1). Accordingly, each model fit two sets of parameters, one for the zero-inflation component and one for the count component. For the analyses of children’s probability of receiving non-sibling childcare, we fit multilevel Bernoulli logit models controlling for child’s age, with random intercepts terms for the child’s ID, their mother’s ID, and the community.

Descriptive summaries of allostatic load mean scores (standard deviation) in different covariate groups by sex at baseline.

We use Monte Carlo methods which draw parameters from Bayesian posterior distributions to generate a distribution of population size estimates and trajectories, thus giving managers a fuller accounting of the uncertainty in the population status. We then propagate this population estimate and its associated uncertainty into a model using Monte Carlo methods to assess the impact of fishing bycatch on the species. We show that the population is below the recovery goal of 3,000 adults. The current total population estimate (including juveniles) is approximately 10,000 fish. Our model finds that fishing bycatch pressure reduces an otherwise assumed stable population by a median value of 0.4% per year, which could impede the recovery of the species. Fisheries bycatch is only one of many threats this population faces, and future work is needed to assess how other threats, such as spawning habitat alteration through dams and water diversions, may affect this population’s trajectory. The framework presented here is suitable for further data integration or modular expansion to incorporate the cumulative effects of challenges facing green sturgeon recovery. Methods We assessed the number of spawning green sturgeon adults in annual surveys of the Sacramento River, CA, USA from the Irvine Finch Boat Ramp (river kilometer 320, just west of Chico) up to Redding (river kilometer 480) (Fig. 1). Acoustic tag data and egg mat studies have confirmed that this is the extent of the spawning grounds (Poytress et al. 2013; Thomas et al. 2014). We surveyed any site along that section of the river with depths greater than 5 m (Erickson et al. 2002). Generally, we see green sturgeon in approximately 40 sites. This section of the river provides the vast majority (effectively all) of the southern DPS green sturgeon spawning locations. 2.3 Spawner survey A detailed description of the methods is published by Mora et al. (2015) and is only briefly described here. The survey has taken place continuously since 2010. There are three phases of the survey conducted over three weeks in mid-June. In phase one, a survey crew drifts downstream over the deepest parts of the channel with a depth sounder. The crew contour maps areas of the river with depths greater than 5 m using a sonar system. The survey generally finds approximately 70 areas with a depth over 5 m within the study area (Fig. 1). The crew marks locations with spawner observations in the last 5 years for an automatic revisit in phase three. In phase two, the crew uses a Dual frequency IDentification SONar (DIDSON; Sound Metrics, Bellevue, Washington) video camera to scan for green sturgeon during 3 passes at sites without spawners in the previous 5 years. In phase three, the crew visits all sites where sturgeon have been seen in the past 5 years as well as any new ones added during phase two. At each site, the crew makes 7 passes recording DIDSON footage. The DIDSON footage from phase three is reviewed in random order three times and counts are combined into an estimate of the number of sturgeon at each site location. The sum of counts from all sites is the total number of spawners observed. 2.4 Life table, IPM, and sensitivity analysis 2.4.1 Literature parameters Both the IPM and life table models need some parameters describing demographics, behavior, and physiology. We took a subset of these parameters directly from the literature (Appendix S1). All these parameters are for the northern DPS green sturgeon as similar data is unavailable for the southern population. 3.4.2 Length vs. age relationship We used the age vs length data for southern DPS green sturgeon on the Sacramento River from supplement 1 of Ulaski and Quist (2021). We fit these data with Bayesian regression in R using JAGS (packages used rjags, purr, ggplot, dplyr, patchwork, viridis, minpack.lm, ggextra, mcmcplots, and furrr) (Plummer 2003; R Core Team 2015; RStudio Team 2015; Wickham 2016b; Elzhov et al. 2016; Wickham 2016a; Curtis 2018; Wickham et al. 2018; Garnier 2018; Attali & Baker 2019; Pedersen 2019; Vaughan & Dancho 2021). The model and priors are as follows:

L ~ normal(Μ_L, Τ) Μ_L = L_∞ (1-e^((-k(A-t_0)))) L_(∞ ) ~ normal(μ = 190 cm, τ = 0.05 (1/cm)) k ~ gamma(ϕ = 1, θ = 0.2 (1/yrs)) t_0 ~ normal(μ = -3 yrs, τ = 0.001 (1/yrs)) Τ ~ gamma(ϕ = 0.001, θ = 0.001 (1/cm))

Eq. 1

where L is the length, ML is the mean of the length distribution, T is the precision of the length distribution, L∞ is the asymptotic length, k is the growth coefficient, A is age in years, and t0 is agee at zero length. Priors are loosely informed by data from northern green sturgeon (Adair et al. 1983; Farr et al. 2002). We ran three MCMC chains with 1000 adaptation steps and 20000 burn-in steps and saved 10000 samples per chain at 90% thinning. Each chain had random starting values based on draws from the prior distributions. All chains converged based on visual inspection or running means. We compared these results to fits for the northern DPS of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.3 Annual survival Appendix S1 presents an estimate of mortality based on a catch curve analysis from a fishery in the Columbia Estuary. We used the telemetry data from the sturgeon on our system to calculate annual mortality. We then used these two values to bookend the estimate of annual mortality in the life table model and IPM. We used data from the BARD for all green sturgeon tags from 2007-2018. We only used data where length was labeled as either total length or fork length. We converted all lengths to fork lengths and all lengths reported in this manuscript are fork lengths. We used the mean parameters from Eq. 1 to convert the lengths to ages. We grouped the data in 5-year bins to reduce noise. Instantaneous mortality is equal to the slope of the change in counts with age after natural-log transformation. We calculated the slope of the descending arm and converted it from instantaneous mortality to annual survival using (annual survival) = exp(-instantaneous mortality) (Ricker 1975). Subsequent calculations involving survival drew mortality from a uniform distribution over the range between the Columbia River and this estimate. 3.4.4 Probability of being an adult Rather than using published ages of maturity or the maturity curve implied by the Beamesderfert al. (2007) cohort model, we based the timing of maturity on data specific to the southern population. We calculated the probability that fish of a certain length are adults (i.e. potential spawners) using the same raw data set from the BARD. We flagged fish as adults if they were marked as “mature”, “adult,” or “eggs” (meaning they were caught with eggs) or if detections were above river kilometer 320 (the bottom of the spawning ground) (note the BARD uses a different river kilometer 0, thus river kilometer 320 equates to 410 in the BARD). Only tagging-year records were used so that length and maturity data were contemporaneous. We grouped individuals by sex into females and others (males and unknown). We obtained two separate estimates of the probability of maturation with length using Bayesian logistic regression. The first estimate was a sex-specific hierarchical model fit divided between females and others in which the sexes shared a common slope but had separate intercepts. We used the female parameters from this model in the sensitivity analysis calculation because that analysis needed fecundity. The second estimate was a fit with all the data for use in the population estimate, which needed the total number of spawners. The models are as follows:

P ~ Bernoulli(Μ_A) Μ_A = (1+e^(-(b_0+b_1 L)))^(-1) b_(1 ) ~ normal(μ = 0 (1/cm), τ = 10^(-12) cm) b_0 ~ normal(μ = 0, τ = 10^(-12))

Eq. 2

where P is the probability of being an adult, MA is the mean of the probability distribution, L is the length, b0 is the intercept and b1 is the slope. Thus, in the hierarchical model, females and others share the b1 term but have separate b0 terms. We used broad normal priors (Eq. 2). We ran three chains with 1000 adaptation steps and 20000 burn-in steps and saved 10000 samples at 90% thinning per chain. Each chain had random starting values based on draws from the prior distributions. All chains converged. We compared these results to fits for the northern population of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.5 Spawning interval distribution The population estimate portion of the IPM needs the distribution of spawning intervals for all adults and the sensitivity analysis requires the average spawning interval of females. To calculate these data, we took all detections from the BARD above river kilometer 320 for months during the spawning season (March - September). We removed any detections from the same year the fish was tagged as well as any detections for fish without a detected outmigration between upstream records. For each fish, we then found both the interval between tagging and the first return and, if available, the interval between the first and second return. We divided these into two groups (all fish and females). We then constructed a distribution showing the fraction of fish that have a return interval greater than each interval and calculated the average return interval. We compared these results to fits for the northern population of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.6 Calculate the fraction of the population that is adults and spawners We then sampled from the estimated distributions of survival, maturation, and spawning interval described above to make 10,000 life tables (the average parameter values converged at approximately 8,000 samples) from which we calculated the fraction of the population that is

Prey selection is a fundamental aspect of ecology that drives evolution and community structure, yet the impact of intraspecific variation on the selection for prey size remains largely unaccounted for in ecological theory. Here, we explored puma (Puma concolor) prey selection across 6 study sites in North and South America. Our results highlighted the strong influence of season and prey availability on puma prey selection, and the smaller influence of puma age. Pumas in all sites selected smaller prey in warmer seasons following the ungulate birth pulse. Our top models included interaction terms between sex and age, suggesting that males more than females select larger prey as they age, which may reflect experiential learning. When accounting for variable sampling across pumas in our 6 sites, male and female pumas killed prey of equivalent size, even though males are larger than females, challenging assumptions about this species. Nevertheless, pumas in different study sites selected prey of different sizes, emphasizing that the optimal prey size for pumas is likely context-dependent and affected by prey availability. The mean prey weight across all sites averaged 1.18 times mean puma weight, which was less than predicted as the optimal prey size by energetics and ecological theory (optimal prey = 1.45 puma weight). Our results help refine our understanding of optimal prey for pumas and other solitary carnivores, as well as corroborate recent research emphasizing that carnivore prey selection is impacted not just by energetics but by the effects of diverse ecology. Methods GPS Programming and Identifying Puma Prey We programmed GPS collars to obtain location data at 1- or 2-hr intervals (i.e., 12 or 24 locations/day). GPS data was transmitted through an Argos uplink at 3-day intervals in Patagonia and Mendocino, or 2-6 times per day via Iridium and Globalstar uplinks for the remaining sites. We identified aggregated GPS points, termed GPS clusters (Anderson Jr and Lindzey 2003), via visual assessments in GoogleEarth or ArcGIS, except in Siskiyou and Washington, where we employed a Python script (Python Software Foundation Hampton, NH) to assess GPS data and identify clusters. We defined clusters as any ³2 points within 150 m of each other spanning 2-hrs to two weeks, except in Wyoming and Washington, where we identified clusters that spanned 4-hrs to 2 weeks, and Mendocino, where identified clusters spanned 8-hrs to 2 weeks. Researchers investigated GPS clusters in the field using handheld GPS units to navigate to sites, and assessed hair, skin, rumen, and bone fragments to identify prey species and sex. We differentiated predation from scavenging based upon associated signs, including bite marks, blood splatter, and signs of chase or struggle (Elbroch et al. 2013). Ungulate prey age was determined based on tooth eruption sequences and lower mandible wear, with individuals ³3 years considered as adults (Elbroch et al. 2013). We determined prey weights from the published literature and, in some cases, utilized ungulate neonate growth curves (Table A.1; A.2). Statistical Analyses We evaluated 10 a priori candidate models (Table 1) that tested varying aspects of our three hypotheses in R Statistical Software (Version 4.2.2 R Core Team 2022). To determine whether pumas killed larger prey in winter, in sites where larger prey were available, and with increased age (our first hypothesis), we utilized the fixed effect variables season, site (i.e., research site), max prey (prey availability) and puma age. We examined the prediction that males will select larger prey than females (our second hypothesis) using variable sex and interaction terms sex*age, as well as sex*max prey. To test our third hypothesis, we calculated mean prey size that pumas utilized at both the site and the multi-site level. We determined seasonal classifications (season) based on ungulate parturition dates at each site, which occur in late May and early June for ungulates, including deer and elk across California, Wyoming, Washington, and Colorado (Hines and Lemos 1979; Bowyer 1991; Smith 1994; Whittaker and Lindzey 1999; Peterson et al. 2017), and November and December in Patagonia (Gonzalez et al. 2006; Corti et al. 2010). For northern sites, we defined summer as the 3 months from May 15 until August 15, and then Autumn, Winter, and Spring as the 3-month intervals following summer. In Patagonia, we defined summer as the 3-month interval from November 15 until February 15, and then Autumn, Winter and Spring following at 3-month intervals. We categorized the largest prey available to each puma in its home range (max prey) using a categorical variable that was based on prey weight (3 values: deer, guanaco, elk). We classified puma age (months) using gum line recession measured at captures, following Laundré (2000), or by birthdate for pumas for which we knew this information. We estimated puma age at the time of each kill by adding an individual’s age at capture to the number of days since said capture before the kill was made. We log-transformed age at the time of the kill for analyses. We determined puma sex (M or F) at the capture event. We used Generalized Linear Models (GLMs) with a log-link function and gamma distribution for hypothesis testing. In our gamma regression analyses, we used prey weight (in kg) as the response variable. To estimate prey weight for each prey item that pumas consumed at a site, we excluded prey with neither discernible age nor sex characteristics. We assigned prey with identifiable age characteristics but no discernible sex the median species-specific weight for males and females within that age class. We excluded kill sites with no corresponding date for the kill from this analysis.
We included a random effect for puma (ID) to avoid pseudoreplication and biases introduced by sampling one puma more than another. We used Variance Inflation Factors (VIF) to assess multicollinearity amongst covariates. Amongst correlated covariates, we considered any VIF scores >2 to have large impacts, with VIF >5 considered highly correlated and VIF >10 considered a severe correlation (Graham 2003). We fit all 10 models using the ‘lme4’ package (Bates et al. 2015) in R. We ranked models using Akaike’s Information Criterion corrected for small sample size (AICc). We considered any model within ∆AICc <2 of the lowest AICc model as top models (Burnham and Anderson 2002). We conducted post-hoc ANOVA tests to determine whether pumas selected different prey sizes at different sites. When a significant p-value was generated, we assumed at least two sites had significant differences. To investigate this further, we ran a Tukey HSD test for site comparisons. Finally, we calculated mean prey size for pumas as compared to mean puma weights, to test the assumption that mean prey size would be 1.45 times larger than mean puma weight, following Carbone et al. (1999) optimal prey size estimates.

The data available in connection with the publication are the data for the respective raster maps as well as the data table for modelling. (1) Raster data (count_covar_raster.grd): The data are stored as a raster stack with 9 layers. The layers are named as follows: Layer 1: "subs", the number of submissions Layer 2: "pop", the number of inhabitants Layer 3: "age", the average age of the population Layer 4: "fem", the percentage of the female population Layer 5: "temp", the average temperature in °C from March to November (2012-2017) Layer 6: "preci" the average rainfall in mm from March to November (2012-2017) Layer 7: "wind", the average wind speed in m/s Layer 8: "water", the presence of a larger, standing water body (yes=1, no=0) Layer 9: "east", the location of the grid cell in former political East Germany (yes=1, no=0) in the respective grid cell. (2) Raster data with complete cases for the predictors respected in the Automated modelling selection as data table (model_compl_cases.csv). The column names correspond to the raster data layer names. The variables are additionally described in the "Table Structure" section. The data used for the submission counts are an export of the nationwide database "CULBASE" that contained all data from mosquito monitoring since start of the nationwide monitoring programme in 2011. The CULBASE merged into the new database VECTORBASE in September 2020. Both databases are not publicly.

Effective biomonitoring requires an understanding of the factors driving concentrations of the substances or compounds of interest in the tissues of studied organisms. Biomonitoring of trace elements, and heavy metals in particular, has been the focus of much research; however, the complex roles many trace elements play in animal and plant tissues can make it difficult to disentangle environmental signals from physiology. This study examined the concentrations of 15 trace elements in the teeth of 122 Pacific walruses (Odobenus rosmarus divergens) to investigate the potential for walrus teeth as biomonitors of trace elements in Arctic ecosystems. Elemental concentrations were measured across cementum growth layer groups (GLGs), thereby reconstructing a lifetime history of element concentrations for each walrus. The locations of GLGs were used to divide trace element time series into individual years, allowing each GLG to be associated with an animal age and a calendar year. The elements studied exhibited a great deal of complexity, reflecting the numerous factors responsible for generating tooth trace element concentrations. Generalized linear mixed models were used to investigate the importance of age and sex in explaining observed variation in trace element concentrations. Some elements exhibited clear physiological signals (particularly zinc, strontium, barium, and lead), and all elements except arsenic varied by age and/or sex. Pearson correlations revealed that elements were more strongly correlated among calendar years than among individual walruses, and correlations of trace elements within individual walruses were generally inconsistent or weak. Plots of average elemental concentrations through time from 1945 – 2014 further supported the correlation analyses, with many elements exhibiting similar patterns across the ~70 year period. Together, these results indicate the importance of physiology in modulating tooth trace element concentrations in walrus tooth cementum, but suggest that many trace elements reflect a record of environmental exposure and dietary intake/uptake.

Methods Trace element analysis and data processing

Postcanine teeth from 122 Pacific walruses (Female: n = 93; Male: n = 29) were on loan from the University of Alaska Museum in Fairbanks, Alaska, and the National Museum of Natural History, in Washington DC. Specimens were collected between 1880 and 2016 (Table S1). The majority of these samples originated from Alaska Native subsistence harvests in the communities of Gambell and Savoonga on St. Lawrence Island, Alaska, though some of the earlier specimens were collected during scientific expeditions. Because specimens used in this study originated from museum collections and/or Alaska Native subsistence harvests, this research was Institutional Animal Care and Use Committee (IACUC) exempt. All specimens from contemporary subsistence harvests were transferred to UAF for analysis under a Letter of Authorization from the United States Fish and Wildlife Service (USFWS) to Dr. L. Horstmann.

A low speed, water-cooled saw equipped with a diamond blade was used to create a 1.5 mm-thick longitudinal cross-section of the center of the tooth. A 3000-grit diamond smoothing disc mounted on a rotary polishing wheel was then used to polish this cross-section. Samples were rinsed with ultra-pure water after polishing and allowed to air dry, then rinsed and air dried again immediately prior to analysis.

Trace element analyses were conducted at the Advanced Instrumentation Lab, University of Alaska Fairbanks (UAF), Fairbanks, Alaska. An Agilent 7500ce Inductively Coupled Plasma Mass Spectrometer (ICP-MS; fitted with a cs lens stack to improve sensitivity), coupled with a New Wave UP213 laser, was used to measure concentrations of vanadium (51V), chromium (53Cr), manganese (55Mn), iron (57Fe), cobalt (59Co), nickel (60Ni), copper (63Cu), zinc (66Zn), arsenic (75As), strontium (88Sr), molybdenum (95Mo), silver (107Ag), cadmium (111Cd), barium (137Ba), and lead (208Pb) in walrus tooth cementum. Instrumental precision for the ICP-MS was ± 5 %. The internal standard for these analyses was 43Ca, and the resulting calcium-normalized element concentrations are reported in parts per million (ppm). Measured elemental concentrations were compared with a United States Geological Survey microanalytical phosphate standard (MAPS-4), as well as a National Institute of Standards and Technology Standard Reference Material (NIST SRM 610). All laser transects were ablated using the following parameters: beam width = 25 μm; power = 55 %; pulse frequency = 10 Hz; transect speed = 5 μm/s. Dwell times ranged from 0.002 – 0.15 seconds (Table S2). Locations of ablation transects were selected to maximize distance from the root, where cementum growth layer groups converge and become distorted, while also avoiding areas of tooth wear near the crown, where not all cementum layers are present. Transects were ablated starting at the interface between the dentin and the cementum (first year of life), and ending at the outer edge of the tooth (final year of life). Thus, elemental time series generated during these analyses represented a lifetime record for each animal.

Data extraction and processing was conducted in Igor Pro version 6.37 using the Iolite software package version 3.0. All statistical analyses were conducted using R version 4.0.2 (R Core Team, 2020) with RStudio version 1.3.959 (RStudio Team, 2015). Limits of detection were calculated separately for each analytical run using the standard method applied by Iolite (Longerich et al., 1996). A value of one half the limit of detection was used to replace data points that fell below the detection limits (U.S. Environmental Protection Agency, 2000). Data points more than 4 standard deviations from the mean were considered outliers and removed from analysis (Tukey, 1977). These data points were typically single, unrealistically high values, and were likely to represent instrumental errors, rather than actual changes in tooth trace element concentrations. Their removal is therefore unlikely to have impacted the results of this study.

Growth layer group counts and designation of element concentrations to individual years

After trace element analysis, photographs of walrus teeth were taken using a Leica DFC295 camera coupled with a Leica M165 C optical microscope using reflected light. All growth layer groups (GLGs) in the tooth cementum were identified (Fay, 1982; Garlich-Miller et al., 1993) and marked collaboratively by the authors (C.T.C., L.H., and N.M.), and their positions were revisited on at least two additional days to confirm their locations on the laser ablation transect (Fig. 1). Locations of the growth layers were used to assign measured elemental concentrations to individual years of life, with L1/D1 (the first light and dark layers) representing Age 0 (1st year of life), L2/D2 making up Age 1 (2nd year of life), and so on. All GLGs were counted to estimate the age of each animal at death, and this information was used in tandem with the year of death to associate GLGs with individual calendar years. Thus, an animal that was Age 5 when it was harvested in 1995 would have GLGs grown in 1990 – 1995. Only complete GLGs with a fully formed light and dark layer were used for analysis of trace element concentrations by animal age or calendar year.

Statistical methods

Trace element data were natural log-transformed prior to statistical analysis to ensure their distributions approximated normality. Generalized linear mixed models (GLMMs) were run using the R package ‘lme4’ (Bates et al., 2015) to test for relationships between concentrations of each trace element and individual walrus age, as well as test for differences between males and females. These analyses were restricted to ages 0 – 15, to ensure that ≥15 male and female walruses were represented at each age. Model selection was conducted using Akaike’s Information Criterion corrected for small sample sizes (AICc), where models with the lowest AICc score were considered to best explain the variability in the data (Burnham and Anderson, 2002). In instances where more than one model had a DAICc < 2, the model with the least parameters was selected. Prior to running the GLMMs, random effects were selected using restricted maximum likelihood (“REML = ‘TRUE’” in the ‘lmer()’ function) and using AICc selection on the fully-parameterized models with varying random effects. Random effects tested included random intercepts for individual ID (“(1|id)”) and calendar year (“(1|year)”), and a combination of both of these intercepts, as well as correlated (“(age|id)”) and uncorrelated (“(age||id)”) random intercepts and slopes for individual ID by animal age, with and without a random intercept for calendar year. After choosing the random effects, model selection was conducted on five models with varying combinations of fixed effects for individual age and sex (Table S3). Both Sr and Ba exhibit large, non-linear changes in early life associated with nursing and weaning (Clark et al., 2020b), thus GLMMs were only conducted for ages ≥ 5, where the weaning signal is no longer present in the data. Individuals with five or more elemental concentrations classified as outliers (i.e., falling more than 4 standard deviations from the mean concentration of all individuals; Tukey, 1977) were excluded from the GLMM for that element. This resulted in the omission of one individual from the GLMMs for Cu and Pb. Model predictions and 95% confidence intervals were calculated using the ‘bootpredictlme4’ R package, which uses a bootstrapping approach (1000 iterations, in this case) to generate confidence intervals (Duursma, 2017).

Pearson’s correlations were used to investigate relationships among trace elements within the lives of individual walruses, among

PLEASE, CITE AS Kalabikhina IE, Kuznetsova PO, Zhuravleva SA (2024) Size and factors of the motherhood penalty in the labour market: A meta-analysis. Population and Economics 8(2): 178-205. https://doi.org/10.3897/popecon.8.e121438

Explanatory note 1: List of papers used in the meta-analysis - see the file "Meta_regression_analysis_papers".

The data is presented in WORD format.

Explanatory note 2: Set of data used in the meta-analysis - see the file "Meta_regression_analysis_table".

The data is presented in EXCEL format.

Description of table headers:

estimate_number - Number of the estimate

paper_number - Number of the paper

paper_name - Paper (year and first author)

paper_excluded - Paper was excluded from the final sample

survey - Data source

table_in_paper - Number of the table with the regression results in the paper

coeff - Regression coefficient for parenthood variable (estimate)

se - SE of the estimate

t - t-value of the estimate

ols - Estimate is obtained using the OLS method

fixed_effects - Estimate is obtained using the fixed effects method

panel - Model considers panel data (for several years)

quintile - Estimate is obtained using the quintile regression method

other - Estimate is obtained using other methods

selection_into_motherhood - Estimate is obtained allowing for selection into motherhood

hackman - Estimate is obtained allowing for selection into employment (Heckman procedure)

annual_earnings - Annual earnings are considered in the model

monthly_wage - Monthly wage is considered in the model

daily_wage - Daily wage is considered in the model

hourly_wage - Hourly wage is considered in the model

min_age_kid - Child's age (minimum)

max_age_kid - Child's age (maximum)

motherhood - Model uses a dummy variable of the presence of children

num_kids - Model uses a variable of the number of children

kid1 - Model uses a variable of the presence of one child

kid2p - Model uses a variable of the presence of two or more children

kid2 - Model uses a variable of the presence of two children

kid3p - Model uses a variable of the presence of three or more children

kid3 - Model uses a variable of the presence of three children

kid4p - Model uses a variable of the presence of three or more children

race/nationality - Model includes a race/ethnicity variable

age - Model includes the age variable

marstat - Model includes the marital status variable

oth_char_hh - Model includes any other variables of other household characteristics

settl_type - Model includes a variable of the type of settlement (urban, rural)

region - Model includes a variable of the region of the country

education - Model includes information on the level of education

experience - Model includes a variable of work experience

pot_experience - Model includes a variable of potential work experience, to be calculated from the data on age and number of years of education

tenure - Model includes a variable of the duration of employment at the current job

interruptions - Model includes a variable of employment interruptions (related to motherhood)

occupation - Model includes an occupation variable

industry - Model includes a variable of the industry of employment

union - Model includes a variable of trade union membership

friendly_conditions - Model includes a variable of the favourable working conditions for mothers (flexible schedule, possibility to work from home, etc.).

hours - Model includes a variable of the number of hours worked

sector - Model includes a variable of the type of employer ownership (public or private)

informal - Model includes a variable of informal employment

size_ent - Model includes a variable of the employer size

min_age_woman - Woman's age (minimum)

max_age_woman - Woman's age (maximum)

mean_age_woman - Woman's age (mean)

restricted - Sample is limited

private - Model considers only private sector employees

state - Model considers only public sector employees

full_time - Model considers only full-time workers

part_time - Model considers only part-time workers

better_educated - Model considers only women with a high level of education

lower_educated - Model considers only women with a low level of education

married - Model includes only married women

single - Model includes only single women

natives - Model includes only native women (born in the country)

immigrants - Model includes only immigrant women (born abroad)

race - Model includes only women of a particular race

min_year - Time period (minimum year)

max_year - Time period (maximum year)

journal - Type of publication

usa - Sample includes women from the USA

western_europe - Sample includes women from Western Europe (Belgium, France, Germany, Luxembourg, the Netherlands, Switzerland)

north_europe - Sample includes women from Northern Europe (Denmark, Finland, Norway, Sweden)

south_europe - Sample includes women from Southern Europe (Greece, Italy, Portugal, Spain)

east_centre_europe - Sample includes women from Central or Eastern Europe (Czechia, Hungary, Poland, Russia, Serbia, Ukraine)

china - Sample includes women from China

Russia - Sample includes women from Russia

others - Sample includes women from other countries

country - Country name

General linear model of the effects of fertility rate and adult female mortality on the mean age at menarche calculated for 89 countries.

Models predicting disease transmission are vital tools for long-term planning of malaria reduction efforts, particularly for mitigating impacts of climate change. We compared temperature-dependent malaria transmission models when mosquito life history traits were estimated from a truncated portion of the lifespan (a common practice) to traits measured across the full lifespan. We conducted an experiment on adult female Anopheles stephensi, the Asian urban malaria mosquito, to generate daily per capita values for mortality, egg production, and biting rate at six constant temperatures. Both temperature and age significantly affected trait values. Further, we found quantitative and qualitative differences between temperature-trait relationships estimated from truncated data versus observed lifetime values. Incorporating these temperature-trait relationships into an expression governing the thermal suitability of transmission, relative R0(T), resulted in minor differences in the breadth of suitable temperatures for Plasmodium falciparum transmission between the two models constructed from only An. stephensi trait data. However, we found a substantial increase in thermal niche breadth compared to a previously published model consisting of trait data from multiple Anopheles mosquito species. Overall, this work highlights the importance of considering how mosquito trait values vary with mosquito age and mosquito species when generating temperature-based suitability predictions of transmission.

Methods Life history experiment

We ran a life history experiment on the urban type form Anopheles stephensi, which was initiated three days after adult emergence to permit mating. After they were presented with an initial blood meal for 15 minutes via a water-jacketed membrane feeder, we randomly distributed 30 host-seeking females into individual cages (16 oz. paper cup; mesh top) to one of six constant temperature treatments (16°C, 20°C, 24°C, 28°C, 32°C, 36°C ± 0.5°C, 80% ± 5 RH, and 12L:12D photoperiod). Each individual adult cage contained an oviposition site: a petri dish secured to the bottom of the housing containing cotton balls to retain liquid, overlaid with filter paper for egg removal and counting. Individual mosquitoes were offered a blood meal for 15 min each day. Blood meals were scored through visual verification of the abdomen immediately after the feeding period. Oviposition sites were rehydrated and checked for the presence of eggs daily. We followed populations of individual females in each temperature treatment until all mosquitoes had died or when less than 7% of the starting population remained. At least two biological replicates were performed at each temperature (N= 390).

Statistical analyses

We used generalized linear mixed models (GLMM) R package

Fitting thermal responses in a Bayesian framework

To predict the thermal limits (Tmin, Tmax) and optimum (Topt) for each parameter, we used Bayesian inference to fit either a symmetric (quadratic; -c(T-Tmin)(T-Tmax)) or an asymmetric (Briere; cT(T-Tmin)(Tmax-T)1/2) unimodal non-linear function to each trait versus temperature (T, in degrees Celsius) as in Johnson et al. 2015 (10). Note the parameter c is a fit parameter that controls the shape of each respective function. These functions were further restricted to be non-negative. That is, all traits are assumed to be zero if T < Tmin or T > Tmax). We assumed that data are distributed as truncated normal distributions with the means for each block and temperature described by either the quadratic or Briere function as above. We selected the best-fitting functional form for the mean between quadratic or Briere using the Deviance Information Criterion (DIC). We chose to fit thermal responses to the data means across individuals for each replicate as opposed to the raw individual data due to 1) the data exhibiting extreme non-normality for some traits (e.g., lifetime egg production, estimated daily eggs, and lifespan) and thus 2) to ensure compliance with the central limit theorem (CLT) when fitting truncated normal distributions. Developing methodology to account for the non-normal distributions associated with observing individual level data is a key research gap to refine predictions of thermal suitability of transmission events and is an area of future work.

For each parameter in the mean function (i.e., c, Tmin, Tmax) and the variance of the truncated normal distribution, we assumed relatively uninformative uniform priors that restrict the range of parameters to biologically meaningful values. More specifically, we first fit curves with uninformative priors restricted to biologically informed ranges (T0 ~ uniform (0, 24), Tm ~ uniform (25, 45), c ~ uniform (0, 1)) , followed by informative priors derived from traits estimated in a previous study (assuming a gamma distribution over each component trait). A direct comparison of each temperature-trait response using either uninformative or informative priors is provided to illustrate the influence of informative priors on our trait fits presented in the main text. No fit with informative priors was conducted for lifetime egg production (B) as Johnson et al. 2015 did not fit this trait and thus appropriate priors did not exist. Further, we choose to use the fit using uninformative priors for estimated daily eggs (EFD*) as informative priors altered the thermal response outside the observed data and drastically increased the credible intervals. This is likely associated with the large uncertainty associated with the prior fit observed in Johnson et al. 2015.

Models were fitted in R using JAGS/rjag, which implements Markov Chain Monte Carlo (MCMC). For each thermal trait, posterior draws for the parameters were obtained from three concurrent Moarkv chains. In each chain, a 5,000-step burn-in phase was followed by 20,000 samples of the stationary chain, for a total of 60,000 posterior samples. These samples were then thinned by saving every eigth sample, in order to further reduce autocorrelation in the chain and to reduce computation in the following analyses.

We also defined temperature-trait responses for mosquito and parasite traits not directly measured in this study to assess the impact incorporating multiple trait thermal responses from a single mosquito species (An. stephensi), rather than aggregated from several different mosquito species, has on relative R0(T). An. stephensi data in Paaijmans et al. 2013 and Shapiro et al. 2017 were used to construct temperature-trait relationships for mosquito development rate (MDR), probability of egg to adult survival (pEA), P. falciparum development rate (PDR) and vector competence (bc). In contrast, for the Multi-species estimated model we used the thermal relationships defined in Johnson et al. 2015.

Childhood and adolescent overweight and obesity are one of the most serious public health challenges of the 21st century. A range of genetic, family, and environmental factors, and health behaviors are associated with childhood obesity. Developing models to predict childhood obesity requires careful examination of how these factors contribute to the emergence of childhood obesity. This paper has employed Multiple Linear Regression (MLR), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbour (KNN) models to predict the age at the onset of childhood obesity in Saudi Arabia (S.A.) and to identify the significant factors associated with it. De-identified data from Arar and Riyadh regions of S.A. were used to develop the prediction models and to compare their performance using multi-prediction accuracy measures. The average age at the onset of obesity is 10.8 years with no significant difference between boys and girls. The most common age group for onset is (5-15) years. RF model with the R2 = 0.98, the root mean square error = 0.44, and mean absolute error = 0.28 outperformed other models followed by MLR, DT, and KNN. The age at the onset of obesity was linked to several demographic, medical, and lifestyle factors including height and weight, parents’ education level and income, consanguineous marriage, family history, autism, gestational age, nutrition in the first 6 months, birth weight, sleep hours, and lack of physical activities. The results can assist in reducing the childhood obesity epidemic in Saudi Arabia by identifying and managing high-risk individuals and providing better preventive care. Furthermore, the study findings can assist in predicting and preventing childhood obesity in other populations.

This study examined the experiences of Canadian undergraduate students accessing mental healthcare between November 2022 to February 2023. We specifically assessed the impact of social determinants of health (i.e., gender, socioeconomic status, immigration status, English as a second language). Participants were recruited through social media platforms and by undergraduate program administrators at Canadian universities. Participants were asked to provide demographic information, answer questions about their experiences accessing mental healthcare, and to complete the mental health continuum short form (MHC-SF). Descriptive statistics and linear regression models were used to assess the association between MHC-SF and social determinants of health (e.g.: demographics, language, immigration status). Of 1098 students invited to participate, 365 participants completed the study (completion rate: 33.2%). Their mean age (SD) was 21.4 (4.6) years; 73.6% were female and 45.7% identified as non-White. Overall, the mean (SD) MHC-SF score of participants was 2.36 (0.99) out of 5. Students with low SES had lower MHC-SF scores (mean 2.08 vs 2.45; p = 0.003). The multivariable analysis showed that low SES (β -0.36; 95%CI: -0.60 to -0.12) and female gender (β -0.29; 95%CI: -0.58 to -0.012) were associated with lower MHC-SF scores. Additionally, being White was associated with higher MHC-SF scores (β -0.29; 95%CI: -0.44 to 0.54). Age, English as a second language, and immigration status were not significant predictors of mental health. High levels of stress, negative perceptions of the mental healthcare system, and limited access were the more common reported themes in the qualitative analysis. In our cohort, university students from across Canada had low MHC scores. Social determinants of health (e.g., low SES, being non-White, and identifying as a woman) were independent predictors of low MCH scores. Further studies are needed to identify specific groups at higher risk as well as strategies to overcome the suboptimal mental health among Canadian students.

PurposeTo develop an ensemble machine learning prediction model for clinical refraction in childhood using partial interferometry measurements.MethodsAge, sex, cycloplegic refraction, and partial interferometry data collected within one month were obtained from patients aged 5–16 years, retrospectively. Four ensemble regression models were used to develop prediction models of spherical equivalents (SE) from the collected data. Root mean squared error (RMSE) was used to compare the accuracy among the models. The accuracy of the ensemble models was compared with that of a previously developed multiple linear regression model.Results4156 eyes from 1965 patients (50.3% female) were included. Mean age was 8.4 ± 2.3 years and mean SE was −1.01 ± 2.94 diopters. Mean axial length was 23.63 ± 1.41 mm and mean keratometry reading of flat and steep axis was 43.58 ± 1.40 diopters. Developed ensemble models had accuracy of RMSE 0.800 to 0.829 diopters, which was superior to that of the conventional regression model (1.213 diopters). Simulations with the same biometric parameters showed that female sex was associated more with myopia than that of male sex. Long eyes showed dampened increase in the myopic refraction per unit axial length.ConclusionsRefractive errors can be calculated in the childhood using these ensemble models with ocular biometric parameters. Moreover, the models were able to simulate hypothetical relationships between ocular parameters and SE to understand the nature of clinical refraction.

BackgroundEarly detection of ovarian aging is of huge importance, although no ideal marker or acknowledged evaluation system exists. The purpose of this study was to develop a better prediction model to assess and quantify ovarian reserve using machine learning methods.MethodsThis is a multicenter, nationwide population-based study including a total of 1,020 healthy women. For these healthy women, their ovarian reserve was quantified in the form of ovarian age, which was assumed equal to their chronological age, and least absolute shrinkage and selection operator (LASSO) regression was used to select features to construct models. Seven machine learning methods, namely artificial neural network (ANN), support vector machine (SVM), generalized linear model (GLM), K-nearest neighbors regression (KNN), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to construct prediction models separately. Pearson’s correlation coefficient (PCC), mean absolute error (MAE), and mean squared error (MSE) were used to compare the efficiency and stability of these models.ResultsAnti-Müllerian hormone (AMH) and antral follicle count (AFC) were detected to have the highest absolute PCC values of 0.45 and 0.43 with age and held similar age distribution curves. The LightGBM model was thought to be the most suitable model for ovarian age after ranking analysis, combining PCC, MAE, and MSE values. The LightGBM model obtained PCC values of 0.82, 0.56, and 0.70 for the training set, the test set, and the entire dataset, respectively. The LightGBM method still held the lowest MAE and cross-validated MSE values. Further, in two different age groups (20–35 and >35 years), the LightGBM model also obtained the lowest MAE value of 2.88 for women between the ages of 20 and 35 years and the second lowest MAE value of 5.12 for women over the age of 35 years.ConclusionMachine learning methods combining multi-features were reliable in assessing and quantifying ovarian reserve, and the LightGBM method turned out to be the approach with the best result, especially in the child-bearing age group of 20 to 35 years.

AUC values from the Age Model but setting age to average for the three respective age groups.

ABSTRACT The objective of this study was to select the best non-linear model that fits the growth curve of turkeys managed under the tropical conditions of Southern Mexico. Data from 481 Hybrid converter turkeys (236 females and 245 males) reared under commercial conditions typical of that region were used. Turkeys were given ad libitum access to feed and water. Body weight was weekly recorded from 1 day to 23 weeks of age. Five non-linear mathematical models (Brody, Gompertz, Logistic, von Bertalanffy and Richards) were chosen to describe the age-weight relationship. The Brody and Richards' models fail to converge. The best fitting model was chosen based on the average prediction error (APE); the multiple determination coefficient R2 and the Akaike information criterion (AIC). In both sexes, von Bertalanffy and Gompertz were the best models. The highest estimates of parameter A (mature weight) for both females and males were obtained with the von Bertalanffy model followed by the Gompertz and Logistic. The estimates of A were higher for males than for females. The highest estimates of parameter k (rate of maturity) for both females and males were, in decreasing order for the Logistic, Gompertz, and von Bertalanffy models. k values for female turkeys was higher than for males. The age at the point of inflection and body weight at the age of point of inflection varied with the model used. The largest values of TI and WI corresponded to the Logistic model. Between sexes, the largest TI and WI values corresponded to males. The best models to describe turkey growth were the von Bertalanffy and Gompertz models, because it presented the highest APE, R2 and AIC values.

ABSTRACT Objective To evaluate the psychometric properties of the seven theoretical models proposed in the literature for European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30), when applied to a sample of Brazilian cancer patients. Methods Content and construct validity (factorial, convergent, discriminant) were estimated. Confirmatory factor analysis was performed. Convergent validity was analyzed using the average variance extracted. Discriminant validity was analyzed using correlational analysis. Internal consistency and composite reliability were used to assess the reliability of instrument. Results A total of 1,020 cancer patients participated. The mean age was 53.3±13.0 years, and 62% were female. All models showed adequate factorial validity for the study sample. Convergent and discriminant validities and the reliability were compromised in all of the models for all of the single items referring to symptoms, as well as for the “physical function” and “cognitive function” factors. Conclusion All theoretical models assessed in this study presented adequate factorial validity when applied to Brazilian cancer patients. The choice of the best model for use in research and/or clinical protocols should be centered on the purpose and underlying theory of each model.