Since approximately 40% of the global workforce are women, a comprehensive understanding of association of maternal employment with child dietary patterns, physical activity and sedentary behaviour needs more focus. This systematic review aims to identify the association between maternal employment and dietary patterns (DP), physical activity (PA) and sedentary behaviour (SB) of children and adolescents (6 to 18 years). Searches were performed using electronic databases and manual searches. Peer reviewed journal articles, conference papers, theses at masters/doctoral levels in English were included. A total 42 studies met selection criteria, which indicated associations between maternal employment and at least one of the domains of interest: DP, PA and/or SB. Using individual samples of analysis, it was found that, 9 samples of DP, 11 samples of PA and 12 samples of SB were positively correlated with maternal employment, whereas 25 samples of DP, 5 samples of PA and 5 samples of SB showed an opposite association. Results suggest that PA and SB were positively related with maternal employment, whereas DP had an inverse relationship. Findings from this review provide evidence that children of employed mothers had poorer DP and greater prevalence of SB, however, their children are more physically active. Future interventions need to create a positive environment at the workplace and for families to support employed mothers and improve children’s dietary patterns and decrease sedentary behaviours. Future studies should prioritise the domains of DP, PA and SB that have been studied inadequately and have inconsistent results. Childhood obesity is an emerging salient public health challenge of the 21st century . ChildhoAccording to World Development Indicator , worldwiLiterature from the USA , U.K. H, Canada There has been a visible shift in women’s employment in low to middle-income countries (LMIC) over the past two decades . The incNo systematic review has investigated how dietary patterns, physical activity and sedentary behaviours (in combination) among children are related to maternal employment. Some previous research that has been identified investigated the association of maternal employment with child obesity and discussed solely one or two of these behaviours as influencing factors towards overweight and obesity . HoweverThis systematic review aims to identify the association between the dietary habits, physical activity and sedentary behaviour of children, with the employment status of mothers. Our main research question is, therefore, ‘does the employment status of mothers with children aged 6–18 years affect children’s dietary habits, physical activity and sedentary behaviour?’2The research protocol of this study is registered in PROSPERO, an international prospective register of systematic reviews (registration number: CRD42020145438).2.1PRISMA) guidelines  reported on maternal employment status, and dietary patterns (DP), physical activity (PA), or sedentary behaviour (SB) of children aged 6 to 18 years; (ii) were written in English; (iii) published as a peer reviewed journal article, conference paper, or thesis at masters or doctoral level. All research methods, designs, as well as measurement instruments were included. Studies were not considered for inclusion in the systematic review if: (i) the population of the study was only adults, (ii) had obesity or overweight as the focal point of research outcomes, (iii) published as a literature review, (iv) did not provide information about the age of the study population, (v) full text was not available and (iii) was not written in English.2.3All studies identified by database search and additional searches were screened for eligibility based on title, abstract and full text by two independent reviewers (SA and LB). Any disagreements were resolved by discussion with the other reviewers/authors.meta-analysis.The data for each included study were extracted by the first author using a standardized extraction form and verified by other authors. Data were extracted on: (1) characteristic of publication , (2) sample characteristics , (3) primary and secondary outcomes as well as measurement methods used for dietary patterns, physical activity and sedentary behaviour. A narrative synthesis of results of included studies was provided. The number of studies included in the systematic literature review were too diverse in outcome as well as measurement to pool data to conduct a 2.4Studies with significant associations identified between maternal employment and domains of dietary patterns, physical activity and sedentary behaviour were not discussed unless three or more studies were available (for each category) . ConceptStudies with significant associations between maternal employment and variables  were included in the ‘Related to maternal employment’ column of 2.5Numbers in the second and the fourth columns of 2.6Risk of bias of the included studies was assessed using a modified version of Cochrane Collaboration tool adopted for observational studies following 3The search of bibliographic databases yielded 14,306 potentially relevant citations, with a further 88 identified through the secondary backward reference searching. Full text papers were reviewed for 108 studies, of which 68 were excluded. A further two papers were identified from reviewing reference lists of included papers, providing a total of 42 papers for the review, as shown in 3.1Among the 42 studies included in the review 26 assessed dietary patterns, with 11 assessing dietary patterns using standard dietary pattern questionnaires, three with 24 h dietary recall, two studies used 3-day food diaries, four used food frequency questionnaires (FFQ), two used a healthy eating index (HEI), two used a youth and adolescent food frequency questionnaire (YAQ) and Global school based student health survey (GSHS) questionnaire, and 2-day food diary each were used by one study. Ten studies used self-reporting or parent reporting measures to assess dietary patterns. The review identified ten domains of dietary patterns, and eight were studied three or more times. Snack’s food including fast food & junk food was the most assessed domain of dietary pattern used in 9 studies followed by family meals assessed in 8 studies. Four studies  among niDietary quality showed a positive relationship with maternal employment in one study , a negatIn brief, 28 samples reported negative, 9 samples positive, and 9 samples reported no association between children’s dietary patterns and maternal employment. Overall, the associations between maternal employment and children’s dietary patterns showed more adverse than favourable directions.3.2Nineteen studies  reportedBriefly, 11 samples reported positive association whereas 5 samples observed negative association with maternal employment. Overall, working mothers were more likely to have active children.3.3Twenty studies  reportedIn sum, 13 samples reported positive, 5 samples negative, and 4 samples reported no association between children sedentary behaviour and maternal employment.3.4The World Bank’s income classification of countries was used in this review. Based on this classification, 32 of 42 reviewed studies were from HIC, 7 from UMIC, and 3 from LMIC. 3.5Across all studies, 57.1% (n = 24) had high risk of bias score. The remaining 40.5% studies (n = 17) were classified as low risk of bias score, while 2.4% (n = 1) had unclear risk of bias. Concerning each criterion of risk of bias for observational studies, 56.1% of studies (n = 23) used probability sampling methods and were hence identified as low risk of bias. The majority of the studies (70.8%) had high risk of performance bias as most cases of ‘maternal employment’ were measured using non-validated tools. Similar to performance bias, over half of detection bias (51.2%) had high risk for using non-validated measurements for dietary patterns, physical activity and sedentary behaviour. Over one third of studies  did not manage < 20% of missing data and were marked as high risk for attrition bias. One third of studies  did not provide any reasons for missing data and were identified as unclear risk of attrition bias. Reporting bias is low for two-thirds of the studies  and 17.1% (n = 7) had high risk of reporting bias. Nearly two-thirds of studies  reported about the statistical methods to control for potential confounding factors, and hence were coded as low risk of bias; and 22% (n = 9) did not provide sufficient information regarding confounding factors. Detailed risk of bias results is available in the supplementary material.Effect size is measured for individual studies. Effect sizes ranged from −0.08. to 3.8. Majority of studies fail to produce medium to large effect size.4The aim of this systematic review was to determine whether maternal employment was associated with children’s health behaviours, specifically dietary patterns, physical activity, and sedentary behaviour. A wide range of domains for these behaviours among children within HIC and LMIC were identified. These domains were assessed using various tools and the association with maternal employment was varied. The review shows that the number of studies on these lifestyle variables among children related to maternal employment has expanded in the last two decades, with two studies between 1980s and 1990s, and all remaining studies published in the 2000s.Results showed that maternal employment was inversely associated with children’s family meals , fruits In terms of TV watching in children as a sedentary behaviour, most studies reported positive associations with maternal employment . TV view4.1Applying a comprehensive and systematic approach, this review included a detailed summary and critical narrative synthesis of 42 published papers. Additional strengths of the review are the inclusion of all study designs as well as all type of measures of dietary patterns, physical activity and sedentary behaviour. Furthermore, this review included multiple domains of dietary patterns, physical activity and sedentary behaviour.meta-analysis. Conceptually similar domains were combined which may also narrow down the depth of analysis and generalizability of these findings. Though, multivariate tests are more accurate, use of univariate test for assessing statistical significance is a limitation of this study. Finally, most of the studies are from HIC, hence findings may not be similar in the context of LMIC.This systematic review has some limitations. Although we tried to identify as many studies as possible, we may have inadvertently missed some eligible studies due to limited search strategy and beyond published English language studies. Furthermore, a majority of the studies were cross-sectional, thus conclusions regarding causality of association are not possible. Although device-based measures of physical activity and sedentary behaviour are more reliable, only a small number of studies used them. Most studies used self-report or maternal-report data, hence contributing to the possibility of reporting bias. Risk of bias among included studies were relatively high since many studies did not report on the reliability and validity of measures used to assess maternal employment and dietary patterns, physical activity and sedentary behaviour. The diverse nature of measures and outcomes prevented the use of 5To our knowledge, this is the first systematic review that summarises the evidence for links between dietary patterns, physical activity, and sedentary behaviour of children with the employment status of their mothers. Findings suggest that maternal employment was associated with poor dietary patterns but more physical activity and more time on sedentary activities. The latter were particularly for TV viewing and other screen-based activities of children. These findings provide an indication of how maternal employment may increase the risk of childhood obesity. We also identified a lack of validated measurers of dietary patterns and few studies using device-based assessment of physical activity and sedentary behaviour. The findings of this systematic review have important implications in the context of growing participation of women in the labour force. Considering that the employment and economic activity of women will continue to increase in the future, interventions should support employed mothers with an aim to promote healthier children’s dietary patterns and decrease sedentary time. Little can be deduced from the inadequately studied domains ), hence future studies need to focus on these domains of dietary patterns, physical activity and sedentary behaviour. Domains that have inconsistent results also require further testing. In addition to this, future research needs to pay attention to UMIC and LMIC because research related to maternal employment and children’s lifestyle variables are scarce in those countries. Moreover, the use of device-based measures of physical activity and sedentary behaviour are needed in order to produce accurate estimates of total time spent in physical activity and sedentary behaviour.6This research did not receive any financial support from any organization.7N/A8N/A9Data and other material will be provided as supplementary document.All authors contributed to the planning and design of the systematic review. The corresponding author performed the literature searches, full text screening, data extraction and analysed, and wrote the first draft of the review. The other authors contributed to the screening of the studies for eligibility, to the risk of bias assessment of studies. All authors reviewed, edited, and approved the final draft. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


CDC conducts abortion surveillance to document the number and characteristics of women obtaining legal induced abortions and number of abortion-related deaths in the United States.2019.Each year, CDC requests abortion data from the central health agencies for 50 states, the District of Columbia, and New York City. For 2019, 49 reporting areas voluntarily provided aggregate abortion data to CDC. Of these, 48 reporting areas provided data each year during 2010–2019. Census and natality data were used to calculate abortion rates  and ratios , respectively. Abortion-related deaths from 2018 were assessed as part of CDC’s Pregnancy Mortality Surveillance System (PMSS).A total of 629,898 abortions for 2019 were reported to CDC from 49 reporting areas. Among 48 reporting areas with data each year during 2010–2019, in 2019, a total of 625,346 abortions were reported, the abortion rate was 11.4 abortions per 1,000 women aged 15–44 years, and the abortion ratio was 195 abortions per 1,000 live births. From 2018 to 2019, the total number of abortions increased 2% , the abortion rate increased 0.9% , and the abortion ratio increased 3% . From 2010 to 2019, the total number of reported abortions, abortion rate, and abortion ratio decreased 18% , 21% , and 13% , respectively.In 2019, women in their 20s accounted for more than half of abortions (56.9%). Women aged 20–24 and 25–29 years accounted for the highest percentages of abortions  and had the highest abortion rates . By contrast, adolescents aged <15 years and women aged ≥40 years accounted for the lowest percentages of abortions  and had the lowest abortion rates . However, abortion ratios in 2019 were highest among adolescents (aged ≤19 years) and lowest among women aged 25–39 years. Abortion rates decreased from 2010 to 2019 for all women, regardless of age. The decrease in abortion rate was highest among adolescents compared with any other age group. From 2018 to 2019, abortion rates decreased or did not change among women aged ≤24 years; however, the abortion rate increased among those aged ≥25 years. Abortion ratios also decreased or did not change from 2010 to 2019 for all age groups, except adolescents aged <15 years. The decrease in abortion ratio was highest among women aged ≥40 years compared with any other age group. From 2018 to 2019, abortion ratios increased for all age groups, except adolescents aged <15 years.In 2019, 79.3% of abortions were performed at ≤9 weeks’ gestation, and nearly all (92.7%) were performed at ≤13 weeks’ gestation. During 2010–2019, the percentage of abortions performed at >13 weeks’ gestation remained consistently low (≤9.0%). In 2019, the highest proportion of abortions were performed by surgical abortion at ≤13 weeks’ gestation (49.0%), followed by early medical abortion at ≤9 weeks’ gestation (42.3%), surgical abortion at >13 weeks’ gestation (7.2%), and medical abortion at >9 weeks’ gestation (1.4%); all other methods were uncommon (<0.1%). Among those that were eligible (≤9 weeks’ gestation), 53.7% of abortions were early medical abortions. In 2018, the most recent year for which PMSS data were reviewed for pregnancy-related deaths, two women died as a result of complications from legal induced abortion.Among the 48 areas that reported data continuously during 2010–2019, overall decreases were observed during 2010–2019 in the total number, rate, and ratio of reported abortions; however, from 2018 to 2019, 1%–3% increases were observed across all measures.Abortion surveillance can be used to help evaluate programs aimed at promoting equitable access to patient-centered quality contraceptive services in the United States to reduce unintended pregnancies. This report summarizes data on legal induced abortions for 2019 that were provided voluntarily to CDC by the central health agencies of 49 reporting areas  and comparisons over time for the 48 reporting areas that reported each year during 2010–2019 (47 states and New York City). This report also summarizes abortion-related deaths reported voluntarily to CDC for 2018 as part of the Pregnancy Mortality Surveillance System (PMSS).Since 1969, CDC has conducted abortion surveillance to document the number and characteristics of women obtaining legal induced abortions in the United States. After nationwide legalization of abortion in 1973, the total number, rate , and ratio  of reported abortions increased rapidly, reaching the highest levels in the 1980s, before decreasing at a slow yet steady pace  intended to terminate a suspected or known intrauterine pregnancy and that does not result in a live birth. All abortions in this report are considered to be legally induced unless stated otherwise.In most states and jurisdictions, collection of abortion data is facilitated by a legal requirement for hospitals, facilities, or physicians to report abortions to a central health agency  Gestational age of pregnancy in completed weeks at the time of abortion Race , ethnicity (Hispanic or non-Hispanic), and race by ethnicityMethod type Marital status Number of previous live births Number of previous induced abortions Residence In addition, the template provided by CDC requests that aggregate numbers for certain variables be cross-tabulated by a second variable. The cross-tabulations presented in this report include weeks of gestation separately by method type, by age group, and by race/ethnicity.Beginning with 2014 data, instead of reporting the clinicians’ estimates of gestational age or estimates of gestational age based on last menstrual period, some areas have reported “probable postfertilization age,” “clinician’s estimate of gestation based on date of conception,” and “probable gestational age” to CDC. To ensure consistency between data reported as postfertilization age and the data collection practices for gestational age recommended by CDC’s National Center for Health Statistics  to induce an abortion at ≤9 completed weeks’ gestation consistent with the current Food and Drug Administration (FDA) labeling for mifepristone (implemented in 2016) (Four measures of abortion are presented in this report: 1) the number of abortions in a given population, 2) the percentage of abortions among women by selected characteristics, 3) the abortion rate , and 4) the abortion ratio . Abortion rates adjust for differences in population size. Abortion ratios measure the relative number of pregnancies in a population that end in abortion compared with live birth.,The U.S. Census Bureau estimates of the resident female population were used as the denominator for calculating abortion rates . Cells with a value in the range of 1–4 or cells that would allow for calculation of these values have been suppressed in this report to maintain confidentiality in tables presented by reporting area of occurrence.Trends in the number, rate, and ratio of reported abortions and annual data are presented for the 48 areas that reported every year during 2010–2019. The percentage change in abortion measures from the most recent past year (2018 to 2019) and during the 10-year period of analysis (2010 to 2019) were calculated for these 48 reporting areas.Trends are also reported for abortions by age group, weeks of gestation, and early medical abortions (≤9 completed weeks’ gestation). Annual data are presented for areas that met reporting standards every year during 2010–2019; the percentage change was calculated from the beginning to the end of the 10-year period of analysis (2010–2019), in 5-year increments from the beginning to the end of the first and second halves of this period (2010–2014 and 2015–2019), and from the most recent past year (2018 to 2019). Consistent with previous reports  were from 48 reporting areas that provided data every year during 2010–2019. In 2019, these continuously reporting areas had an abortion rate of 11.4 abortions per 1,000 women aged 15–44 years and an abortion ratio of 195 abortions per 1,000 live births . From 20In 2019, a considerable range existed in abortion rates by reporting area of occurrence  and abortion ratios  . The perAmong the 49 areas that reported abortion numbers by women’s age for 2019, women in their 20s accounted for the majority (56.9%) of abortions . Women aAmong the 44 reporting areas that provided data each year by women’s age for 2010–2019, this pattern across age groups was stable, with the majority of abortions and the highest abortion rates occurring among women aged 20–29 years and the lowest percentages of abortions and abortion rates occurring among those in the youngest and oldest age groups . From 20Among the 47 areas that reported age by individual year among adolescents for 2019, adolescents aged 18–19 years accounted for the majority (70.2%) of adolescent abortions and had the highest adolescent abortion rates  . AdolescAmong the 30 areas that reported race by ethnicity data for 2019, non-Hispanic White women and non-Hispanic Black women accounted for the largest percentages of all abortions , and Hispanic women and non-Hispanic women in the other race category accounted for smaller percentages  . Non-HisAmong the 42 areas that reported by marital status for 2019, 14.5% of women who obtained an abortion were married, and 85.5% were unmarried . The aboAmong the 45 areas that reported the number of previous live births for 2019, 40.2%, 24.5%, 20.0%, 9.2%, and 6.0% of women had zero, one, two, three, or four or more previous live births . Among tAmong the 43 areas that reported gestational age at the time of abortion for 2019, 79.3% of abortions were performed at ≤9 weeks’ gestation, and nearly all (92.7%) were performed at ≤13 weeks’ gestation . Fewer aAmong the 47 areas that reported by method type for 2019 and included medical abortion on their reporting form, 49.0% of abortions were surgical abortions at ≤13 weeks’ gestation, 42.3% were early medical abortions , 7.2% were surgical abortions at >13 weeks’ gestation, and 1.4% were medical abortions at >9 weeks’ gestation; other methods, including intrauterine instillation and hysterectomy/hysterotomy, were both uncommon (<0.1%) . During Among the 42 areas that reported abortions categorized by individual weeks of gestation and method type for 2019, surgical abortion accounted for the largest percentage of abortions within every gestational age category, except ≤6 weeks’ gestation . At ≤6 wIn selected reporting areas, abortions that were categorized by weeks of gestation were further categorized by age and by race/ethnicity . In everUsing national PMSS data . InvestiThe annual number of deaths related to legal induced abortion has fluctuated from year to year since 1973 . BecauseFor 2019, a total of 629,898 abortions were reported to CDC by 49 areas. Of these reporting areas, 48 submitted data every year for 2010–2019, thus providing the information necessary for consistently reporting trends. Among these 48 areas, for 2019, the abortion rate was 11.4 abortions per 1,000 women aged 15–44 years, and the abortion ratio was 195 abortions per 1,000 live births. From 2018 to 2019, the number of abortions increased 2%, the abortion rate increased 0.9%, and the abortion ratio increased 3%. Although the rate of reported abortions declined overall from 2010 to 2019, after reaching a historic low in 2017, the abortion rates increased overall between 2017 and 2019.Approximately 18% of all pregnancies in the United States end in induced abortion . However, the abortion ratio among women aged ≥40 years continues to be higher than among women aged 25–39 years. These data underscore important age differences in abortion measures.The trends in adolescent abortions described in this report are important for monitoring trends in adolescent pregnancies in the United States. From 2010 to 2019, national birth data indicate that the birth rate for adolescents aged 15–19 years decreased 51% , when the risks for complications are lowest  or do not report the data in a manner consistent with this guidance . Missing demographic information can reduce the extent to which the statistics in this report represent women who have had abortions. Findings in this report on the age of women seeking abortions were generally similar to previously published data from Guttmacher Institute’s national survey of abortion patients in 2014, although the percentage of abortions among non-Hispanic Black women was lower and among Hispanic women was higher compared with data provided to CDC  is not possible.Ongoing surveillance of legal induced abortion is important for several reasons. First, abortion surveillance can be used to help evaluate programs aimed at promoting equitable access to patient-centered contraceptive care in the United States to reduce unintended pregnancies. Cost, inadequate provider reimbursement and training, insufficient patient-centered counseling, lack of youth-friendly services, and low client awareness of available contraceptive methods are reported barriers to accessing contraception  and at different gestational ages provides the denominator data that are necessary for analyses of the relative safety of abortion practices (


Lung stereotactic body radiation therapy is characterized by a reduction in target volumes and the use of severely hypofractionated schedules. Preclinical modeling became possible thanks to rodent-dedicated irradiation devices allowing accurate beam collimation and focal lung exposure. Given that a great majority of publications use single dose exposures, the question we asked in this study was as follows: in incremented preclinical models, is it worth using fractionated protocols or should we continue focusing solely on volume limitation? The left lungs of C57BL/6JRj mice were exposed to ionizing radiation using arc therapy and 3 × 3 mm beam collimation. Three-fraction schedules delivered over a period of 1 week were used with 20, 28, 40, and 50 Gy doses per fraction. Lung tissue opacification, global histological damage and the numbers of type II pneumocytes and club cells were assessed 6 months post-exposure, together with the gene expression of several lung cells and inflammation markers. Only the administration of 3 × 40 Gy or 3 × 50 Gy generated focal lung fibrosis after 6 months, with tissue opacification visible by cone beam computed tomography, tissue scarring and consolidation, decreased club cell numbers and a reactive increase in the number of type II pneumocytes. A fractionation schedule using an arc-therapy-delivered three fractions/1 week regimen with 3 × 3 mm beam requires 40 Gy per fraction for lung fibrosis to develop within 6 months, a reasonable time lapse given the mouse lifespan. A comparison with previously published laboratory data suggests that, in this focal lung irradiation configuration, administering a Biological Effective Dose ≥ 1000 Gy should be recommended to obtain lung fibrosis within 6 months. The need for such a high dose per fraction challenges the appropriateness of using preclinical highly focused fractionation schedules in mice. Thoracic radiation therapy exposes healthy lung tissue to ionizing radiation, responsible for the development of radiotherapy-associated side effects such as radiation-induced pneumonitis and fibrosis –3. HistoTherapeutic management of early stage lung tumors has radically changed in recent years, with the use of stereotactic body radiation therapy (SBRT) for the treatment of early stage non-small cell lung cancer [NSCLC ]. SBRT iIn addition to changes in lung target volume, SBRT is characterized by the use of severe hypofractionation. With its parallel organization, the lung is known to be a good candidate for such changes in fractionation schedules . HoweverWith a view to opening up the possibilities for future research, the question we addressed in this study work was as follows: in incremented models of preclinical SBRT exposure with reduced target volumes, is it worth using fractionated protocols or should we continue focusing solely on volume limitation? In this study, we show that using a 3 fractions/1 week regimen, a dose per fraction of at least 40 Gy is necessary for the development of lung fibrosis within 6 months, a reasonable time lapse given the mouse lifespan. No dose-effect was seen for the parameters measured, suggesting a threshold effect for lung fibrosis development. The need for such a high dose per fraction challenges the appropriateness of using preclinical highly focused fractionation schedules in mice.Male C57BL/6JRj mice from Janvier Labs (France) were used for all experiments. Animals were housed in the IRSN animal facilities accredited by the French Ministry of Agriculture for performing experiments on rodents. Animal experiments were performed in compliance with French and European regulations on the protection of animals used for scientific purposes (EC Directive 2010/63/EU and French Decree 2013–118). All experiments were approved by Ethics Committee #81 and authorized by the French Ministry of Research under the reference APAFIS#13021-2018011217442982 v1 .Irradiation was performed on the SARRP  using arc therapy. The muriplan treatment planning system was calibrated by Xstrahl during the commission of the SARRP platform. For that, the reference dose rate was measured in dose to water with a cylindrical ionization chamber and depth doses using EBT3 radiochromic films for each collimator were performed. After this calibration carried out by the company, we have completed the characterization of the SARRP by making Half Value Layer measurements (HVL) and reference dosimetry measurements with ionization chamber following the AAPM TG61 protocol . Moreove® 2%, Bayer Healthcare, France) to create profound anesthesia and limit respiratory motion as far as possible. Anesthetized mice were immobilized on a restraint treatment bed and the isocenter was placed on the left lung on the CBCT image using the treatment planning system Muriplan®, by taking anatomical landmarks for reference. CBCT images were obtained using an uncollimated beam (20 × 20 cm), a high voltage of 60 kV, an intensity of 0.8 mA with an inherent and additional filtration of 0.8 and 1 mm of beryllium and aluminum respectively, with continuous beam on and 360°  stage rotation between the x-ray source and the digital flat panel detector. A total of 236 projections were obtained and a 3D reconstruction image of the mouse was transferred to the Muriplan® dose planning, verification and delivery system. After image segmentation into air, lung, fat, tissue and bone, we then placed the isocenter within the left lung. Irradiations were performed at 220 kV and 13 mA with inherent and additional filtrations of about 0.8 and 0.15 mm of beryllium and copper respectively.For irradiation, mice were anesthetized using 100 mg/kg ketamine  and 10 mg/kg xylazine , with different doses per fraction, i.e., 20, 28, 40 or 50 Gy, delivering total doses of 60, 84, 120 or 150 Gy respectively. The fractionation schedules were chosen based on the results obtained by Lavigne et al. . Indeed,An arc therapy treatment using a 3 × 3 mm collimator with immobile stage and 220° gantry rotation from −110 to 110° was planned for each fraction. To be able to irradiate the same region on the left lung, CBCT images of the first fraction were co-recorded and manually superimposed over the day's CBCT images.Plateforme d'imagerie du vivant, Université Paris Descartes, Faculté de Chirurgie Dentaire) were acquired at 90 kV, 160 μA, no additional filtration and with a field of view of about 20 × 20 × 20 mm for a resolution of 40 μm. The estimated shooting time was about 4 min and 30 s and 512 slices were acquired. The mean dose absorbed by the mice was about 1653 mGy. On the Quantum GX2 (IRSN), images were acquired at 90 kV, 88 μA, with an additional filtration of 0.06 mm of copper + 0.5 mm of aluminum and with a field of view of about 36 mm with a reconstruction of 25 mm allowing for a resolution of 50 μm. The estimated shooting time was about 4 min and 512 slices were acquired. The mean dose absorbed by the mice was about 900 mGy of dose to water.Mice were anesthetized by inhalation of 1.5% isoflurane. Mouse lung imaging was performed using two micro-CT scanners  with respiratory gating. On the Quantum FX, images  histograms were extracted using a bin width of 1-HU. For density distribution representation, smoothing curves were constructed using PRISM software applying second-order smoothing with 60 neighbors.TM RNA Stabilization Reagent  pending analysis. To avoid variations in the structural/cellular constitution of different areas of the lung, age-matched control/unirradiated mice were included for lung imaging and histological analyses and measurements were performed in matched areas between irradiated and non-irradiated mice.Studies were performed 6 months post-exposure. Given the small size of the irradiated area, different animals were dedicated to histological analyses and mRNA preparations. For histology, the right and left lungs were fixed in 4% paraformaldehyde and embedded in paraffin. Five-micrometer paraffin tissue sections were used for HES and Masson's trichrome staining and for immunohistological studies. For the purposes of mRNA preparation, the irradiated area, ipsilateral and contralateral lung tissues were frozen in an RNAlaterTM2000 image analysis software . Immunostaining of club cells  and type II pneumocytes  was used to monitor parenchymal and epithelial lung damage.HES and Masson's trichrome-stained tissue sections were used to assess overall lesion severity. The thickness of the alveolar septa was measured using the Visiol@bUGB-positive cells were counted on the bronchiolar epithelium both inside and far from the injured area, taking into consideration at least three different bronchioles. UGB-positive cells were expressed as a percentage of total cells. The total number of cell nuclei per 100 μm was also tracked in order to monitor radiation-induced variations in total epithelial cell density. SFTPC-positive cells were counted both inside and far from the injured area with between 3 and 5 different fields (X 400 magnification).Total RNA was prepared using the mirVana isolation kit . After quantification on a NanoDrop ND-1000 apparatus , reverse transcription was performed with 1 μg of RNA using a reverse transcription kit from Applied Biosystems (France). Relative mRNA was quantified using the ΔΔCT method with 18S as the housekeeping gene. Genes were chosen as lung cell markers or according to their proven role in lung tissue inflammatory response to radiation exposure.post-hoc test) or Student's t-test, with a level of significance of p < 0.05.Data are expressed as means ± SEM. Statistical analyses were performed by ANOVA  seems to be a threshold for focal lung fibrosis development within 6 months  may suggest the notion of threshold. Only fibrosing schemes were associated with significant club cell depletion, as previously observed following single dose focal lung exposure . Club ceAs mentioned above, we are aware that BED calculation remains controversial for very high doses per fraction or single doses , 50. HowIf we consider a reasonable number of fractions and overall treatment time, the fractionation schedules that can be used in preclinical models of SBRT with the objective of inducing tissue fibrosis within a reasonable time lapse cannot be more representative of clinical practice than previously published single doses. Considering that whole and hemi-thorax exposures already used doses in the ablative range, these new models of focal radiation exposure will influence tissue response more by volume reduction than by changes in the doses delivered and/or the schedule applied. We will probably have to accept being far from the schedules/doses used in a clinical setting and will probably have to consider volume-related clinical relevance rather than dose/schedule-related relevance. The remaining questions tend toward tumor-bearing models. Does a schedule exist that could be used to target a lung tumor while irradiating a sufficient volume of normal tissue, allowing enough time for side effects to develop? This warrants further exciting study.The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author.The animal study was reviewed and approved by Ethics Committee #81 and French Ministry of Research under the reference APAFIS#13021-2018011217442982 v1.AF and FM contributed to the conception and design of the study. MD constructed the model. AB, MD, SB-C, VB, GT, and AF collected and interpreted the data. AF and AB wrote the first manuscript draft. FM, MD, VP, and OG realized critical review of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This work was supported by the ROSIRIS program, with funding from IRSN, Cancéropôle d'Ile de France and INCA, Institut National du Cancer (INCa 2018-1-PL BIO-06-).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


N = 47) and validated (N = 27) an emotion-induction procedure, and recorded (N = 42) posed and spontaneous facial expressions of happy, angry, and sad emotional states. Our novel analysis pipeline quantified the speed of changes in distance between key facial landmarks. We observed that happy expressions were fastest, sad were slowest, and angry expressions were intermediate. In Experiment 2 (N = 67) we replicated our results for posed expressions and introduced a novel paradigm to index communicative emotional expressions. Across Experiments 1 and 2, we demonstrate differences between posed, spontaneous, and communicative expression contexts. Whereas mouth and eyebrow movements reliably distinguished emotions for posed and communicative expressions, only eyebrow movements were reliable for spontaneous expressions. In Experiments 3 and 4 we manipulated facial expression speed and demonstrated a quantifiable change in emotion recognition accuracy. That is, in a discovery (N = 29) and replication sample (N = 41), we showed that speeding up facial expressions promotes anger and happiness judgments, and slowing down expressions encourages sad judgments. This influence of kinematics on emotion recognition is dissociable from the influence of spatial cues. These studies demonstrate that the kinematics of facial movements provide added value, and an independent contribution to emotion recognition.The kinematics of peoples’ body movements provide useful cues about emotional states: for example, angry movements are typically fast and sad movements slow. Unlike the body movement literature, studies of facial expressions have focused on spatial, rather than kinematic, cues. This series of experiments demonstrates that speed comprises an important facial emotion expression cue. In Experiments 1a–1c we developed ( Facial emotion expression and recognition play an important role in successful social interaction , providiIt is well established that spatial features of facial expressions play a key role in emotion recognition. A body of work has previously identified key facial action units (actions of groups of muscles) that reliably map on to discrete emotional expressions , while oIn addition to static, facial expressions also contain dynamic cues see . Since sA small body of work demonstrates that temporal cues play an important role in emotion recognition. For example, across a large number of trials Jack and colleagues  present To date, studies have not explicitly quantified the contribution of kinematic information to facial emotion expression; for example, to the best of our knowledge, studies have not compared the speed of movement of face features  between emotions. However, some insight can be gained from the body movement literature. With respect to body movements, evidence suggests that kinematics differ as a function of emotional state: faster body movements are associated with anger and happiness, while slower movements are indicative of sadness . FurtherIn any study of facial expression, differences that may occur as a function of the context in which the facial expression is produced, should not be overlooked. For instance, the extant literature highlights potential differences between the kinematics of spontaneous, posed and communicative  facial expressions. Such differences may be important in real-world applications of facial emotion processing. For example, although emotion-tracking software typically aims to detect spontaneous expressions, much of our knowledge comes from the posed expressions of professional actors, meaning that tracking software may be suboptimally trained with respect to spontaneous expressions. Early evidence suggests posed expressions to be more asymmetrical than spontaneous expressions . SimilarN = 47) and validates (N = 27) a new set of contemporary emotion induction videos that elicit discrete emotions of happiness, anger and sadness. Subsequently Experiment 1c (N = 42) demonstrates that (a) the speed of face movement differs as a function of emotion, (b) some face actions  are better than others  with respect to differentiating emotions and, (c) while the general pattern of emotion-related speed differences is comparable for spontaneous and posed expressions, there are some differences in the utility of particular face actions. Experiment 2 (N = 67) comprises a partial replication of Experiment 1: that is, in an independent sample, we demonstrate differences in the kinematics of posed happy, angry and sad expressions. Experiment 2 also goes beyond this to show differences in speed between happy, angry, and sad for communicative expressions wherein participants are instructed to express emotion in spoken verbal utterances. Finally, in Experiments 3 (N = 29) and 4 (N = 41), we demonstrate that the emotion-related speed differences observed in Experiments 1 and 2 are influential cues that are used, alongside spatial cues, in emotion recognition. That is, we demonstrate that manipulating the speed of emotional expressions causally impacts on the accuracy of emotion recognition.The following collection of studies aims to first investigate whether speed differs as a function of emotion for posed, spontaneous, and communicative facial expressions (Experiments 1a–1c and 2) and thus establish whether kinematic cues, specifically speed, are indicative of an individual’s emotional state. Second, we aim to investigate whether manipulating speed and spatial cues impact on emotion recognition judgments (Experiments 3 and 4), and thus establish whether both kinematic, as well as spatial, cues are influential in observers’ judgments of emotion. To give a brief overview, Experiment 1 identifies  took part in an online task designed to select videos to induce specific emotional states in the observer. Participants had previously expressed willingness to take part in psychological research at the University of Birmingham and consent was received online, prior to task completion. Experimental procedures, for all experiments, were approved by the local research ethics committee (ERN 16-0281AP5) and were carried out in accordance with the principles of the revised Helsinki Declaration .not at all and 10 indicated very. For valence ratings, 1 indicated highly negative and 10 indicated highly positive.An online rating task included five videos selected to induce each of three target emotions . Videos were presented in a pseudorandom order (6 possible video orders); each was followed by rating scales. Participants were required to rate, in a random order, how happy, angry, sad, disgusted, surprised, and neutral they felt after each video . Participants also rated valence (positive/negative) and arousal levels following each video. Ratings were made on a 10-point Likert scale, whereby 1 indicated SEM) = 7.88(0.15)] were significantly higher (more positive) than the scale midpoint . Ratings for angry [mean(SEM) = 2.68(0.12)] and sad [mean(SEM) = 2.81(0.17)] videos were rated significantly lower (more negative) on the scale relative to the midpoint , sad: . Emotion-specific induction was successful (p < .001). The video providing the highest discreteness score for each target emotion was selected as a “winning” video and used in Experiment 1b .Mean valence ratings for happy videos , 2 [mean(SEM) = 5.59(0.19)], and 3 [mean(SEM) = 5.11(0.19)] were not significantly different from the midpoint  on the scale . In line with Experiment 1a, valence ratings for the happy video [mean(SEM) = 7.59(0.42)] were significantly higher (more positive) than the midpoint , while those for the angry [mean(SEM) = 2.37(0.23)] and sad [mean(SEM) = 2.41(0.34)] videos were rated significantly lower (more negative) on the scale relative to the midpoint .Valence ratings for neutral filler Videos 1 . Happy expressions had the highest speed, sad expressions were the slowest and angry expressions were intermediate  = 119.12, p < .001, ηp2 = .74]: lip raise actions were fastest, mouth widen and eyebrow widen were slowest, and mouth open and nose lengthen were intermediate. There was no main effect of condition (p = .76). An Action × Emotion interaction indicated that the speed of eyebrow widening , mouth widening , and mouth opening , but not lip raising (p = .38) or nose lengthening (p = .08), differed as a function of emotion. Speed of eyebrow widening was highest for angry, lowest for sad and happy, while speed for mouth movements were highest for happy, followed by angry and lowest for sad. However, a significant interaction between condition, emotion, and action was also observed .Replicating Experiments 1a and 1b, emotion induction was successful: emotion rating discreteness scores for each video were significantly greater than zero  = 18.17, p < .001, ηp2 = .31] and mouth widening  only . Bonferroni-corrected post hoc t tests revealed speed of both mouth opening and widening, during the posed condition, to be highest for happy expressions when compared to both angry (mouth widening ; mouth opening ) and sad (mouth widening ; mouth opening ). For mouth opening, angry expressions also elicited higher speed of movement compared to sad . No such differences were observed for spontaneous expression production . See Separate RM-ANOVAs, for each action, revealed Condition × Emotion interactions for mouth opening . Supporting results from Experiment 1c, happy and angry expressions were the fastest and sad expressions were the slowest. A main effect of face action was also observed : eyebrow widen actions were slowest, lip raise and mouth open were the fastest, while mouth widen and nose lengthen were intermediate. Utilizing the new spoken form of facial emotion expression, intuitively in this dataset, there was a main effect of condition ; whereby facial movement speed was higher during spoken compared to posed emotion expressions. The RM-ANOVA revealed a significant main effect of emotion  interaction. Follow-up RM-ANOVAs demonstrated (a) a significant interaction between emotion and face action for both posing  and spoken  conditions, (b) a significant Emotion × Condition interaction for all face areas , and (c) a significant Face Action × Condition interaction for happy , angry , and sad . However, the way in which speed was modulated across face regions differed as a function of condition and emotion  = 1.45, BF01(mouth open) = 3.15, BF01(lip raise) = 4.20) and happy was significantly faster than both angry  and sad . Bayes factors (BF01) here provide a ratio of likelihood for the observed data under the null compared to the alternative hypothesis. Values of 3–10 and 1–3 will be taken as moderate and anecdotal evidence for the null hypothesis respectively , it was also consistently the case that happy and angry were comparably fast  = 6.57, BF01(mouth open) = 2.82) and sad was slower than angry . With respect to lip raise actions, Bayesian statistics provided anecdotal evidence that angry and happy expressions were comparably fast , angry was faster than sad, t(66) = 4.53, p < .001, but happy was not faster than sad . In other words, for both posed and spoken expressions happy mouth movements were fastest, sad was slowest and angry was intermediate. However, for posed expressions, differences between the emotions were driven by happy being faster than sad and angry; for spoken sentences, differences were driven by sad been slower than happy and angry.Spoken sentences differed from this pattern. Although it was also true that for spoken sentences mouth opening and widening movements were significantly faster for happy versus sad  and faster for angry . Whereas for spoken, we observed anecdotal evidence that speed was comparable for angry and sad  and faster for happy .For eyebrow widening we saw a different pattern wherein, for posed expressions, replicating the result from Experiment 1c, speed was comparable for happy and sad , eyebrow movements during emotional spoken utterances are faster for happy compared to angry and sad expressions, and angry lip raise actions are faster than those for happy and sad utterances.The data presented thus far provide evidence that humans exhibit differences in facial movement speed across emotional expressions. Despite some differences across emotional expression contexts , across different face actions, happy and angry expressions are consistently typified by high-speed movement compared to the low speed movement observed for expressions of sadness. Thus, it appears that as well as the presence of specific face actions , compared to static, displays of facial emotion . It is cIt has previously been demonstrated that disrupting the temporal order of action units within a sequence negatively impacts emotion recognition . Thus, tMoreover, an issue that may be adding noise to the literature is that studies where face kinematics have been manipulated often employ stimuli where features such as identity, gender, age and other observable demographic information can provide additional cues about the underlying emotional state . The morN = 29] and Experiment 4: replication [N = 41]) addressed the question of whether kinematic, in addition to spatial, information is used in facial emotion expression recognition. We present an emotion recognition task that removes identity information through the use of point light displays. These were created from recordings of the faces of actors who were asked to convey happy, angry and sad emotional states while verbalizing a neutral sentence. We then created both a spatial manipulation  and a kinematic manipulation (increasing and decreasing movement speed) from the same original stimuli; crossing these factors when creating task stimuli. Participants were required to rate  the extent to which they believed each stimulus depicted each of our target emotions . This (multiple-emotion rating) removed the requirement for participants to make single fixed-choice emotion responses, leaving them free to rate any intensity on each scale from Not at all happy/angry/sad to Very happy/angry/sad. This also facilitates an examination of the impact of manipulating spatial and kinematic features on subtle emotion confusions. Emotion recognition scores were calculated as the rating given for the target emotion minus the mean of the two nontarget emotions.The following two experiments , recruited via the University of Birmingham Research Participation Scheme, gave written informed consent to take part and were reimbursed with a course credit or monetary incentive for their time. Sample size was calculated based on the study by p2 of 0.23 in the main interaction of interest), power of 0.95 and error probability of 0.01, a minimum sample size of 22 participants is required (G*Power 3.0.10).Twenty-nine healthy student volunteers . To create the stimuli we then displayed these successive frames of coordinates as white dots at 25 FPS on a black background screen (using Cogent graphics for MATLAB) and saved these displays as video files to be presented during the computerized task. We will refer to these stimulus videos as point light display faces (PLFs). Stimulus videos of spoken sentence expressions displayed facial movement without sound, a method previously found to successfully convey the intended emotion (The computerized task was programmed using Cogent and Cogent Graphics (Wellcome Department of Imaging Neuroscience), run using MATLAB 2018a , on a Stone  computer and presented on a 21.5-in. iiyama display monitor . Stimuli were created from videos (made using a Sony Handycam HDR-CX240E) of four actors  instructed to hold their head still while expressing one of three target emotional states  via a spoken sentence (“My name is John and I’m a scientist”). Video recordings of each expression were fed into OpenFace from which the  emotion .The kinematic manipulation comprised three levels . When creating stimulus videos for K1, K2, and K3 we saved PLF frames at varying frame rates to create the illusion of varying facial movement speed. Videos for kinematic Level 2 (K2) were saved at the original 25 FPS (as described above), while videos for K1 and K3 kinematic levels were created by reducing and increasing the frame rate by 50% such that K1 was 12.5 FPS and K3 was 37.5 FPS. The stimuli varied in total length, however, length of display has previously been shown in both a point light display task of whole-body emotion movement  and dynax and y coordinates for each of the 68 facial landmarks for each frame as recorded by OpenFace. However, for stimulus videos at S1 and S3 spatial levels, the movement of each facial landmark from one frame to the next was reduced  or increased  to create the apparent reduction or increase in spatial exaggeration of the facial emotion expressions recorded. For each successive pair of frames we calculated the Euclidian distance (radius) according to The spatial manipulation also comprised three levels . Stimulus videos for S2 were created by presenting k = 0.5 for S1 and k = 1.5 for S3. That is, by applying the multiplication factor, k, to the radius, but not to θ the original trajectory of movement of each landmark was maintained while the spatial extent was reduced or increased. Beginning at the original starting location, this manipulation was then applied sequentially to each sample in the stimulus video, for each point on the face. Note that for the Cogent Graphics coordinate system the origin  is in the top left of the computer screen.The new spatially manipulated location of the dot was given by Stimuli were created from four actors expressing each target emotion  for each kinematic manipulation level , at each level of spatial manipulation . This resulted in 108 video stimuli (average length of 2 s per video).Not at all happy/angry/sad and the opposite end representing Very happy/angry/sad. Participants were asked to rate the three emotions of happy, angry and sad, with scales for these presented on the screen in a random order, after each stimulus video. They were not required to make these three ratings sum to 10 and thus ratings were independent of one another. Participants first completed three practice trials, before completing 108 randomly ordered experimental trials across three blocks.Participants were seated 80 cm from the computer monitor, whereby PLF stimuli subtended an average visual angle of 7.51° along the vertical plane and 7.87° along the horizontal plane. This stimuli presentation corresponds to roughly the size of these facial features on an average human face. Participants performed an emotion perception task of roughly 40 min. Each trial in the experiment  began wiNot at all and 10 a response of Very). Emotion recognition scores were calculated as the target emotion rating minus the mean of the two nontarget emotion ratings. For example, for a trial on which a happy PLF was presented, the mean ratings of the two nontarget emotions (angry and sad) were subtracted from the target emotion, happy.Emotion rating responses for each emotion scale on each trial were transformed into scores from 0 to 10  = 10.50, p < .001, ηp2 = .27] with recognition scores highest for happy [mean(SEM) = 4.65(0.34)], lowest for angry [mean(SEM) = 3.14(0.19)] and intermediate for sad expressions [mean(SEM) = 3.85(0.25)]. A main effect of spatial manipulation was observed : S3 stimuli elicited highest recognition scores [mean(SEM) = 5.18(0.21)], followed by S2 [mean(SEM) = 4.11(0.24)] and then S1 [mean(SEM) = 2.34(0.18)]. However, this was qualified by a significant Emotion × Spatial interaction  indicating that for happy and angry videos, recognition scores were highest for high spatial exaggeration S3, followed by S2 and finally S1; whereas for sad videos low spatial exaggeration S1 elicited highest emotion recognition scores, while S2 and S3 were equally rated less accurately. Means are presented in p = .266).RM-ANOVA was used to analyze emotion recognition accuracy with within-subjects factors emotion , kinematic manipulation  and spatial manipulation . This revealed a significant main effect of emotion . Follow-up RM-AMOVAs conducted separately for each emotion revealed a significant main effect of kinematic manipulation for happy , angry  and sad . decreased, from K3 through K1.Crucially, in line with our first prediction, we found an Emotion × Kinematic interaction ), when spatial information was unaltered  = 26.99, p < .001, ηp2 = .49]) and when spatial information was exaggerated  = 16.11, p < .001, ηp2 = .37]). When considering only trials at spatial Level 2 , emotion recognition accuracy still increases with increasing speed for both angry and happy stimuli; whereas emotion-specific recognition for sad videos increased as speed decreased .To address the question of the independence of spatial and kinematic effects on accuracy, we explored Spatial × Kinematic and Emotion × Spatial × Kinematic interactions, neither of which were statistically significant  = 5.36, p < .001, d = 1.43] and angry stimuli . However, for sad stimuli the direction of the effect was reversed such that kinematic manipulation more greatly affected accuracy than spatial manipulation . Note, however, that this effect does not reach significance at a Bonferroni-corrected alpha cutoff of 0.0167 for the three t tests . This analysis suggests that whereas judgments of happiness and anger are more greatly influenced by spatial versus kinematic manipulation, this is not the case for judgments of sadness. For sadness there is a trend toward a greater effect of kinematic over spatial manipulation.To quantify whether accuracy is more affected by kinematic or spatial manipulations, we ran two further analyses. First, examining the absolute change in accuracy from the highest to lowest spatial levels (S3–S1) at the kinematically unmanipulated (K2) level relative to the absolute change in accuracy from the highest to lowest kinematic levels (K3–K1) at the spatially unmanipulated (S2) level, revealed that spatial manipulation (at 100% [K2] speed) more greatly affected accuracy than kinematic manipulation  for happy  and angry . Thus, confirming that for angry and happy expressions the spatial manipulation affects accuracy even when kinematic differences are controlled for. Based on the significant emotion by spatial interaction in our main ANOVA, whereby sad expression accuracy is enhanced by reduced spatial exaggeration (S1 relative to S3), we would expect to find the opposite effect for sad expressions. However, if in line with our original hypothesis that spatial exaggeration may lead to increased recognition accuracy across emotions, we would alternatively expect to find that higher accuracy is observed at S3 compared to S1 when controlling for speed. We indeed found that sad accuracy increased from spatial Level 1 to 3, in the same direction as the effect observed for happy and angry . Although again note that this effect is only trending toward significance at a Bonferroni-corrected alpha cutoff of 0.0167.The second analysis took this one step further by investigating the effect of spatial manipulation while controlling for the speed of movement of facial features. Since speed and distance are intrinsically linked, our spatial manipulation also affects the speed of movement. That is, trials in the S1 condition are at 50% of spatial movement and thus facial features move 50% slower than normal (100% [K2] speed), whereas, trials in the S3 condition are at 150% of spatial movement and 50% faster than normal. Comparing S3 K1 and S1 K3 controls for speed because for both conditions speed is 75% of normal (100% [K2]) speed.Together, these two analyses suggest that, while clear effects of kinematics can be seen across all emotions, recognition accuracy for happiness and anger is more greatly influenced by spatial versus kinematic manipulation. Consequently, for these emotions spatial effects on accuracy are observed even when the speed of the facial features is matched. For sadness, however, there is a trend toward a greater effect of kinematic over spatial manipulation. The omnibus ANOVA shows that spatial exaggeration  reduces accuracy for sad stimuli. However, when the speed of facial features is matched between conditions there is a trend toward greater accuracy for spatially exaggerated stimuli.F = 25.62, p < .001, ηp2 = .48] and are more likely to be confused for sad . Conversely, as the speed of sad videos increases they are rated as less sad  and are more likely to be confused for angry . No such differences were observed in happy ratings across different kinematic levels of angry or sad videos .Our final exploratory question, as previously investigated with whole-body dynamic emotional stimuli , regardsIn sum, in line with previous literature, we demonstrate that spatial features of facial expressions play an important role in facial emotion recognition. However, as predicted, we also demonstrate that kinematic features of facial movement  independently contribute to emotion recognition accuracy. This contribution can be distinguished from the influence of spatial information and indeed persists across all levels of spatial exaggeration/reduction and when spatial information is not manipulated. Specifically, we observed that increasing the speed of movement improved recognition of angry and happy expressions and impaired recognition of sad expressions; decreasing speed improved recognition of sad expressions and impaired recognition of angry and happy expressions. We also demonstrate that, despite an impact of both spatial and kinematic manipulations across all emotions, happy and angry expression accuracy may be more greatly impacted by spatial exaggeration, while sad expression accuracy more greatly impacted by kinematic manipulation. A possible explanation for this effect is that, as with anger and happiness, spatial exaggeration of sadness improves accuracy and the (opposite) effect observed in the omnibus ANOVA (reduced accuracy for S3 relative to S1) is due to the combination of (a) spatial exaggeration increasing speed and (b) kinematic information presiding over spatial information thus meaning that the increase in speed outweighs any benefit of spatial exaggeration. In Experiment 4 we sought to provide an independent replication of these effects of speed on emotion recognition.Mage = 20.88), recruited via the University of Birmingham Research Participation Scheme, gave written informed consent to take part and were reimbursed with a course credit or monetary incentive for their time. We aimed to replicate the kinematic effects found in Experiment 3, while not manipulating spatial information. Considering the presence of our main interaction effect of interest in the previous experiment , and a priori power analyses that revealed a required sample size of 11 to replicate the interaction effect in an independent sample , an increased sample size was chosen in the current experiment on the basis that increasing the sample size should improve the precision of effect size estimation in a replication sample  instructed to express three target emotional states  via a spoken sentence (“My name is John and I’m a scientist”). In this version of the task, we used only stimuli from the kinematic manipulation , not the spatial manipulation. This resulted in 36 PLF video stimuli (average length of 2 s).Participants were seated 80 cm from the computer monitor, precisely as described in Experiment 3. They performed an emotion recognition task of roughly 15 min. The trial procedure followed exactly that of Experiment 3 minus the presence of spatially manipulated PLF stimuli. Participants first completed three practice trials, before completing 36 main experimental trials in one block.Not at all and 10 a response of Very). Emotional recognition scores were calculated as the target emotion rating minus the mean of the two nontarget emotion ratings.As in Experiment 3, emotion rating responses for each probed emotion scale on each trial were transformed into scores from 0 to 10  = 5.46, p = .011, ηp2 = .12]. Happy expressions were rated most accurately [mean(SEM) = 5.04(0.33)], angry expressions were rated least accurately [mean(SEM) = 3.82(0.21)] and sad expressions were intermediate [mean(SEM) = 4.79(0.27)]. There was also a main effect of the kinematic manipulation , whereby recognition accuracy was highest for K2 [mean(SEM) = 4.77(0.18)], and K3 stimuli [mean(SEM) = 4.63(0.19)] and lowest for K1 [mean(SEM) = 4.26(0.20)].RM-ANOVA was used to analyze emotion recognition accuracy with within-subjects factors of emotion  and kinematic manipulation . This revealed a significant main effect of emotion . Emotion-specific recognition scores increased with increasing speed, from K1 through K3, for both angry and happy stimuli; whereas for sad videos, emotion-specific recognition scores increased as speed decreased, from K3 through K1. See As in Experiment 3, we found an Emotion × Kinematic interaction  and more likely to be confused for sad  and as speed of sad videos increases they are rated as less sad  and are more likely to be confused for angry. No such differences were observed in happy ratings across different kinematic levels of angry videos (p = .85), though sad videos, with increasing speed, were marginally more likely to be rated as happy . This difference is driven by the difference between K1 and K3. Finally, while happy videos were no more likely to be confused for anger when speed was increased (p = .54), decreasing the speed of happy videos did elicit increased ratings for sadness .Further supporting results from Experiment 3, all videos were most highly rated for the correct target emotion and the same emotion confusions were observed between angry and sad videos. That is, as speed of angry videos decreases they are rated as less angry [In a series of four experiments, we demonstrate an important role for movement speed in both the production and perception of facial emotion expressions. Experiment 1 demonstrates the development and validation of a new video-emotion-induction protocol. As well as recording posed expressions, we induced emotion-specific states for happiness, anger and sadness, as indicated by self-reported emotional state ratings. Utilizing a novel analysis pipeline, we analyzed the movement speed of key face actions on a frame-by-frame basis. In line with previous findings regarding the kinematics of emotional expression from whole-body movement , across Our data are consistent with current theories whereby the presence of key spatial action units can be used to differentiate facial emotion expressions , but higWith respect to the generalizability of the current findings it is important to consider cultural differences which exist in the production of facial emotion expressions. For example, as previously mentioned, not only is the temporal order of key action units within a sequence diagnostic of specific emotions, but the temporal order has been shown to differ between cultures. For example, Jack and colleagues demonstrated that East Asian individuals use increased early eye region activity to represent emotional intensity, while Western individuals do not . Thus, iNevertheless, data from Experiments 1 and 2 have important implications for expression-centered adaptive gaming and learning platforms that employ facial emotion tracking. The extent to which the kinematics  of facial expressions, or the rate at which the emotion unfolds across time, carry important information about emotional state is currently unclear. While there have been some attempts to combine spatial and temporal features into facial landmark detection and tracking algorithms e.g., , many soImportantly, in the current study, mouth movements only differentiated posed and communicative, not spontaneous, emotions. Thus, although mouth movements may be a key cue for detecting the emotional state of another individual with whom we are engaging in a communicative, conversive social interaction, it should be noted that algorithms aiming to detect spontaneous emotions expressed in a context devoid of social interaction, should not rely on posed expression data sets for training purposes. To date, facial landmark detection algorithms, which have considered temporal as well as spatial features of face movement, have tended to focus on posed expressions of facial emotion e.g., .More broadly, it is important to consider the context in which automatic emotion recognition software is employed and whether contextual factors are likely to elicit communicative or noncommunicative emotional expressions. Here we demonstrate that kinematic cues may indicate different emotions depending on the context. For example, high-speed eyebrow movements were indicative of anger in posed and spontaneous expression contexts, and happiness in a communicative context. This is particularly relevant for our understanding of how to train expression recognition algorithms; these should be trained in a context-dependent fashion. Further investigation of the combination of presence/absence of spatial action units and of the kinematics of these actions across emotions and cultures may lead to exciting advances in algorithmic emotion-recognition across various contexts.Experiments 3 and 4 used point light display stimuli of human communicative facial emotion expressions to investigate the impact of manipulating facial expression speed on emotion-specific recognition accuracy. In a discovery (Experiment 3) and replication sample (Experiment 4), we showed that speeding-up facial expressions promotes anger and happiness judgments, and slowing-down expressions encourages sad judgments. Incorporating a cross-factor design to our stimuli generation, whereby three kinematic levels were generated at each of three spatial levels of exaggeration, in Experiment 3, we demonstrate that the influence of kinematics on emotion recognition is dissociable from the influence of spatial cues. That is, there was no significant interaction between spatial and kinematic factors. Thus, the kinematics of facial movements provide added value, and an independent contribution, with respect to emotion recognition. We also tentatively demonstrate that different emotions may benefit more or less from spatial and kinematic exaggeration, with happy and sad expression accuracy relying more on spatial, and sad expression accuracy more on kinematic, information.These findings complement previous investigations that highlight the importance of the temporal order of emotional facial expression . PreviouOur findings are in line with those from the whole-body emotion recognition literature. Parallel work from It is important to acknowledge here that, while the current perception task makes an attempt to investigate blended perceptions between emotion categories by inviting participants to rate each stimulus in terms of the intensity of happy, angry, and sad emotions, emotion labels are still used and free responses regarding a participant’s perception of the expressions is not sought. Further investigation and future studies are required to confirm the degree to which kinematic and spatial features map on to precise emotion categories versus simply aiding an individual to pinpoint where, on a hypothetical 3D space of arousal, valence and intensity, a facial expression may lie. Related to this is the important consideration of cultural differences in emotion categorisation, whereby a universally recognisable set of six emotions has been refuted. East Asian cultures appear to exhibit both altered perceptual scanning patterns of facial expressions  as well One caveat is that the current work employed point light representations of spoken sentences with the audio soundtrack removed. It is important to note that, unlike spontaneously induced dynamic facial expressions, expressions during spoken utterances are usually also accompanied by audible speech, which adds another layer of information to aid in emotion perception. For example, commonly considered acoustic parameters which relay information about emotion in speech include frequency, amplitude and tempo , and whiThe current research paves the way for a better understanding of individual differences in facial emotion expression production and perception. For example, individuals with autism spectrum disorder often exhibit differences in facial emotion expression recognition  as well In conclusion, speed of facial movement differentiates happy, angry, and sad emotions and contributes to emotion recognition independently of spatial cues. Despite some systematic differences across regions of the face and emotional expression contexts, happy and angry expressions typically show fastest movement, while sad expressions exhibit low speed movement. Moreover, exaggerating these emotion-specific speed profiles leads to improved emotion recognition accuracy. Thus, we see the importance of movement speed in both the production and recognition of facial emotion. In conjunction, speed cues in both production and recognition of facial emotion expressions may serve to guide social interaction partners in understanding and dynamically adapting to one another’s emotional states. We believe these findings to be of importance for both our understanding of typical facial emotion expression as well as in the increasing application of algorithms designed to detect and track facial emotion. Finally, a greater understanding of the spatial and kinematic signatures of emotional expressions, across communicative and noncommunicative contexts, may provide an important framework for the future study of facial emotion in various clinical conditions.10.1037/emo0000835.supp


The Oriental hornet is a notorious bee predator that is well adapted to arid habitats. Under favorable conditions, due to ongoing climate change, it is actively spreading northward in Europe and Central Asia and was recently introduced to the Americas. As a eusocial insect, it forms annual colonies consisting of a foundress queen, sterile workers, brood, and, towards the end of the colony’s life-cycle, also male reproductives. In the present study, we examined nutritional processes in the Oriental hornet, focusing on the neglected caste of male reproductives, to assess the involvement of nutrition in the colony’s reproductive success. We used stable isotope labelled nutrients to investigate the metabolic processes occurring in adult males as they mature inside the nest, assessing the protein digestion capacity in the male and worker castes and evaluating the contribution of larvae to this process. The results showed that, in the body of maturing males, dietary amino acids are utilized first in the synthesis of proteins, then carbohydrates are allocated into tissues as energy reserves. We discovered that adult workers can process dietary proteins independently (contrary to previous knowledge), while males cannot and rely entirely on the larvae to process proteins for them. Larval secretions were found to contribute significantly to protein uptake by both males and workers. We suggest that larval secretions are a central nutritional resource for adult hornets that play a key role in maintaining the social structure in the colony.Males of social Hymenoptera spend the first days following eclosion inside the nest before dispersing to find a young queen to mate with. During this period, they must acquire enough nutrients to enable their sexual maturation and store energy to sustain them through their nuptial journey. It was previously argued that adult hornets are unable to process dietary proteins and rely on the larvae to supply them with free amino acids and carbohydrates that they secrete via trophallaxis. Using isotopically enriched diets, we examined nutrient allocation and protein turnover in newly-emerged males of the Oriental hornet during their maturation period and tested the protein digestion capability in the presence and absence of larvae in both males and worker hornets. The results indicated that protein turnover in males occurs during the first days following eclosion, while carbohydrates are incorporated into body tissues at higher rates towards the end of the maturation period. Additionally, we found that males cannot digest protein and depend on larval secretions as a source of nutrition, while workers, in contrast to previous reports, can metabolize protein independently. Our findings demonstrate the contribution of adult male nutrition and larval secretions to colony fitness.  Apis mellifera; [Eusocial hymenopterans  have developed caste systems based on a reproductive division of labor. Such a system typically includes a founder female (queen), who reproduces, and her sterile daughters (workers) involved in foraging, nursing, and nest maintenance. Males, the third caste, usually emerge temporarily to mate with other young queens. Although their presence is relatively brief, males perform the fundamental role of fertilizing the queen eggs and spreading the colony’s genes. Thus, the male reproductive and distribution capabilities should have an impact on the fitness of the progeny colony. However, compared to queens, males have received little attention in past research, and most of these studies focused on honeybees .Bombus spp.; [Vespa spp.; [Males of the higher eusocial Hymenoptera emerge sexually immature and spend a period inside the nest before they are able to mate. The length of this period varies across, but also within species, ranging between 6–16 days in honeybees [us spp.; ), and 8–pa spp.; ,4). Duripa spp.; . TherefoVespa orientalis) are incapable of performing gluconeogenesis , and lack the proteolytic enzymes required for protein digestion and, therefore, rely on the larvae as a protein metabolizing unit [Vespa crabro) and questioned the conclusions of Ishay and Ikan [Hornets (family Vespidae) are large, eusocial wasps, in which adults and larvae exhibit strikingly different behaviours and nutritional demands: the sessile, growing larvae are hypothesized to feed on a protein-rich diet, while the highly active adults maintain a high carbohydrate and free amino acid diet . Accordiing unit ,12. Thesand Ikan . HoweverIn the present study, we investigated two main related subjects:(a) The contribution of adult nutrition to the sexual maturation and reproductive success in male Oriental hornets: we examined the allocation and incorporation of two important macronutrients found in larval secretions, glucose, and the essential amino acid leucine, in the bodies of male hornets during their maturation period.(b) The protein digestion capabilities of the adult worker and male hornets: we challenged the controversial issue of protein digestion capacity in adult hornets and their consequent dependence on larvae; we suspected that workers, which are capable of developing ovaries and laying eggs , must haVespa species that occurs in deserts [Our animal model, the Oriental hornet, is a notorious predator of honeybees, naturally distributed throughout the Mediterranean Basin, the Middle East, Arabia, and India ,16. As t deserts , it is w deserts . Orienta deserts . Feeding deserts ,21,22. R deserts ,24,25,26 deserts ,28, rais deserts . ConsideV. orientalis kept in a naturally illuminated container (4.6 × 2.4 × 2.4 m) with controlled temperature (28 ± 2 °C) and humidity (60 ± 5%)—in the Meier Segals Garden for Zoological Research at Tel-Aviv University, between July and October 2020. The colonies were collected, fully-developed, from fields surrounding Tel-Aviv University. The colonies were reared in trapezoidal wooden boxes (14 L) with a front glass wall, supplied with water and building material (soil and paper), and fed with a standard sugar solution (60% inverted sugar) and protein (raw chicken and bumblebees) ad libitum.Hornets for the experiments were reared from captive colonies of 13C1-leucine or 13C1-d-glucose (100 mg 13C1-leucine/13C1-d-glucose per 50 mL standard sugar solution). To test the protein digestion capacity in adult hornets, we prepared labelled proteins by feeding bumblebees (Bombus terrestris) on 13C1-leucine enriched sugar solution (100 mg 13C1-leucine per 25 mL standard sugar solution) for seven days. In 13C-1 leucine, any metabolic modification involves removing carbon no.1 [13C in the bees’ muscle tissues to confirm the incorporation of the labelled amino acid . All 13C traces were purchased from Cambridge Isotope Laboratories, Inc. .To examine nutrient allocation and incorporation in male hornets, we prepared labelled artificial nectars by mixing the standard sugar solution with either bon no.1 ; hence, 13C1-leucine/13C1-glucose) during either the first or last five days of the experiment. The males (N = 50) were taken from four colonies upon emergence and distributed randomly between five boxes, according to the following feeding treatments : (1) Leu 1–5—13C1-leucine enriched nectar on days 1–5; (2) Leu 6–10—13C1-leucine enriched nectar on days 6–10; (3) Glu 1–5—13C1-glucose enriched nectar on days 1–5; (4) Glu 6–10—13C1-glucose enriched nectar on days 6–10; and (5) Control—standard (unlabeled) nectar, for ten days. The control group served as a baseline reference for the δ13C isotope analysis. Groups that received a labelled diet during the first five days were fed with the standard nectar in the following 6–10 days and vice versa. The boxes were also supplied with water ad libitum. After ten days of treatment, males were frozen at −20 °C. Next, flight muscles, brains, fat bodies, and reproductive glands  were dissected and removed using a stereoscope and watchmaker forceps. Abdominal tissues  were carefully washed with double-distilled water to ensure there were no traces of labelled fluid originating from the digestive system. The seminal vesicles and accessory glands from each treatment were pooled from five individuals and dried together in Petri dishes to achieve the minimal dry weight for the isotope analysis (n = 2 for each of the five treatments). The rest of the tissues were placed separately in Petri dishes or Eppendorf tubes (1.5 mL) and dried in an oven at 60 °C for three days minimum. Dried muscle tissues were further treated, as mentioned above. The fat bodies of two males, one from the control and one from the Leu 6–10 group, appeared abnormal and, therefore, were excluded from the analysis . Based on personal observations on males from captive colonies, and supported by previous studies on other species of hornets ,4,31, weN = 69; workers from two separate colonies, males from four mixed colonies) were divided into two groups, according to the following treatments: T1—adult hornets alone (without brood), fed on labelled protein ; T2—adult hornets in a colony (with brood and workers), fed on labelled protein . For each treatment and caste, an additional control group (‘C’) was fed on unlabeled protein (with and without brood) to serve as a baseline reference for the δ13C isotope analysis . The control group served as a baseline reference for the δ13C isotope analysis. Hornets received two bumblebees each day for ten days. Treatment boxes were also supplied with the regular sugar solution and water ad libitum. Each day, the amount of leftover food was recorded to ensure that the protein was consumed. After ten days of treatment, the hornets were frozen at −20 °C. Next, we dissected the flight muscles of the hornets under a stereoscope using watchmaker forceps and dried them in an oven at 60 °C for two days. The dried muscle tissues were ground with a metal pestle into a homogenous powder in 1.5 mL Eppendorf tubes before being loaded into tin capsules for isotope ratio analysis.Workers and males  were loaded into tin capsules. The stable carbon isotope ratios  every ten samples to validate the analyzer’s calibration and accuracy.To measure the δsee also ) were meribed in ,34). Alll scale; ). We ranWe used the Kruskal–Wallis ranked-sum test to analyze the nutrient allocation and incorporation in males, followed by a Wilcoxon test for non-parametric multiple comparisons. For analyzing protein digestion in adult hornets, we used a General Linear Model (GLM) followed by post-hoc pairwise comparisons (Tukey adjusted). The statistical analyses were performed using JMP Pro 15.0 and R 4.0.3  (respectively).Z = −3.7, p < 0.001), brain , and fat tissues . A similar trend was observed in the reproductive glands . In treatment T1 (labelled protein in the absence of brood), workers had significantly higher levels of 13C than in the control group (p < 0.05), while males did not (p = 0.99). This indicates that only workers had incorporated the labelled proteins and can digest protein in the absence of larvae. In treatment T2 (labelled protein in the presence of brood), both workers and males showed significantly higher δ13C values than in treatment T1 , indicating that in the presence of larvae, the leucine from the digested protein is incorporated into muscle tissues at a higher rate.Hornets differed between the castes and the treatments . This indicates that males primarily synthesize proteins de novo for flight muscles, reproductive organs, and the brain after they emerge and then process dietary glucose for storage as fuel reserves (as lipids and glycogen). In honeybees, during the first days following eclosion, drones exhibit a significant increase in their body proteins . Most of13C ratios observed in the group fed on labelled glucose on days 6–10 reflects the various metabolic pathways that glucose molecules can undergo in each tissue. Correspondingly, Panzenböck and Crailsheim (1997) found that the body glycogen in honeybee drones decreases within the first five days, then slowly but significantly increases, specifically in the thorax and head [In contrast, we found that older males seem to allocate the glucose (or glucose-derived carbons) in their body tissues at a higher rate than younger males , indicatand head . In actiand head . In the and head ,43. Accoand head ,44. Of all the processes occurring during male maturation, the development of the reproductive system undoubtedly has the greatest impact on male reproductive success; it generally includes the production of seminal fluids and mucus matter by the seminal vesicles and accessory glands and the migration of spermatozoa from the testes to the seminal vesicles ,46,47. TVespula germanica) cannot consume food by themselves and only receive it from the larvae [We found that the protein digestion capacity of adults is caste-dependent: workers can process protein independently, while males cannot. Consequently, male hornets rely entirely on the larvae and workers to nourish them while they mature inside the nest. Similarly, Montagner 1964) reported that males of the German wasp  reported that during this time, the queen avoids taking food from workers and feeds solely on larval secretions, and when larvae are removed from the nest, the queen dies of starvation . Queens The present study demonstrates the involvement of nutrition in the maturation of male hornets and the nutritional role of larvae in the colony. Newly-emerged male hornets utilize dietary nutrients towards their sexual, physical, and behavioral maturation; protein synthesis occurs mainly at an early age, while the allocation of carbohydrates is higher in mature males. Compared to workers, males were found to be incapable of protein digestion and therefore rely entirely on the nutrients provided by the larvae. We suggest that these larval secretions are essential to the male’s proper development and contribute to its successful performance as the colony’s dispersal reproductive unit. Larval secretions were also found to contribute significantly to the nutrition of adult workers, which had assimilated the protein degradation products at a higher rate in the presence of larvae. Being highly attractive and compatible with the worker’s metabolic demands, larval secretions act as an inducer of alloparental care, serving as a nutritional payback that the workers receive when tending to the larvae. We argue that the nutritional flow among members of the colony, specifically, the reciprocal feeding between larvae and adults, is the mechanism by which social structure is maintained.