Little is known about how different school racial climate experiences influence the critical reflection and subsequent critical action behaviors of racially minoritized youth. Therefore, the current study examined how critical reflection mediated the relationship between school racial climate profiles and critical action behaviors. Participants were 559 Black and Latinx adolescents, aged 13–17 who completed an online survey. Results indicated that critical reflection significantly mediated the relationships between interpersonal interactions  and anti‐racist critical action behaviors. Similarly, the relationships between school racial socialization messages  and anti‐racist critical action behaviors were also mediated by critical reflection. Findings have implications for how dimensions of the school racial climate differentially relate to racially minoritized youth's critical consciousness. The school environment is a critical space for developing youth into productive civic actors in our democratic society. This is important both for the political advancement of our nation as youth move into their careers and for promoting a more socially and racially equitable society and reducing disparities. Although explicit teaching of history, civics, and critical thinking is consequential for the civic development of youth, the racial interactions and practices within the school environment  may also influence youth's understanding of systemic inequities and encourage civic action  recruited from across the United States. Fifty percent of participants identified as female. Procedures were approved by the Institutional Review Board at the second author's university. Participants were recruited through Qualtrics Panels, an online survey delivery service that researchers can use to recruit study participants. Parents were recruited through an email invitation that included the expected duration of the study and the type of incentive available for participation. Parents completed a screening survey; if they had a child between the ages of 13 and 17 who attended public or private school, the parent was asked to give consent for their child to complete the study. The parent was then asked to have their child complete the rest of the survey online. Adolescents then completed demographic information; those who identified as White, African American, Asian American, or Latinx were allowed to continue to the rest of the survey until quotas for their ethnic‐racial group (approximately 250 in each group) were filled. Only Black and Latinx participants are included in the current sample. Parents were compensated in credit that they could redeem through Qualtrics for gift cards and other awards.Participants included 559 Black and Latinx adolescents, aged 13–17  treat students of all races/ethnicities fairly”), cultural socialization , and critical consciousness socialization . All were on a response scale of 1  to 5 (completely true).School racial climate perceptions were assessed using the School Climate for Diversity Scale—Secondary, which has been validated in diverse samples of adolescents . The response scale was 1 (strongly disagree) to 5 (strongly agree). Critical action was assessed using the Anti‐racism Action Scale , communal action , and political action ”). Each item was answered (1) “Yes” or (0) “No.” Scores were summed so that higher scores indicated more action.Critical reflection was measured using the Critical Consciousness Scale  with full information maximum‐likelihood estimation. We estimated the relations between equal status, cultural socialization, and critical consciousness socialization and interpersonal, communal, and political action and estimated the indirect effects of critical reflection as a mediator  = 44.88, p < .001, RMSEA = .091, CFI = .942. The standardized model coefficients can be seen in Tables Descriptive statistics and correlations are shown in Table None of the covariates were significantly related to critical reflection or critical action. As expected, equal status was negatively correlated with critical reflection, such that adolescents who perceived more unfair treatment in their school were more likely to understand societal inequality. Furthermore, critical consciousness socialization (perceptions of opportunities to learn about racism) was associated with higher critical reflection. In contrast to our hypothesis, perceptions of opportunity to learn about one's own culture were negatively related to critical reflection. However, cultural socialization had an independent direct path to political action, such that higher perceptions were associated with more actions such as contacting an elected official.In terms of mediation, critical reflection was positively related to each form of action. The bootstrapped indirect effects, reported in Table The goal of the current study was to explore how school racial climate perceptions were associated with critical reflection and critical action. Specifically, our hypotheses about equal status and critical consciousness socialization were confirmed. Scholars have described how experiences with unfair treatment can alert youth to inequalities in society  and included White, Asian American, and Native American youth. Some studies suggest that Latinx youth pay more attention to racial dynamics and may be more strongly affected by them  were also related to critical reflection (Anyiwo et al., We also found a direct, positive relation between cultural socialization and political action above and beyond the effect of critical reflection. Curricula focused on historical events in a student's culture, like the Civil Rights Movement, may give youth models for effective political action and encourage their involvement. A surprising finding, however, was the negative relation between cultural socialization and critical reflection, which was seen in both the bivariate correlations and the path analysis. Learning about one's culture and being encouraged to show cultural pride have been related to positive academic outcomes when the messages come from parents or teachers Byrd, . FurtherImportantly, in our study critical reflection was a significant mediator of the relation between school racial socialization messages and critical action, which suggests that our Black and Latinx youths' actions were explained in part by their increasing awareness of inequality in society (Diemer et al., The implications of this finding are that educators and parents should be vigilant against calls to reduce or eliminate the teaching of history and current events involving race and racism. For example, in North Carolina, a task force led by the Lieutenant Governor encouraged parents and teachers to “hold the system accountable” by submitting examples of “indoctrination” and “inappropriate content” in K‐12 schools in conjunction with statewide and national efforts to restrict anti‐racist teaching (“F.A.C.T.S. | Lieutenant Governor”, A limitation of this study was that it was cross‐sectional; therefore, we were not able to determine causality. Critical reflection or involvement in critical action influences school racial socialization perceptions. For example, the youth who are most aware of inequality in society may be primed to look for inequality within their schools, such that they are more likely to interpret negative cross‐race interactions as unfair discrimination. These students may also be more likely to seek out lessons about their own cultures at school and judge the school negatively when they do not see them. Some scholars feel that cross‐sectional data are not appropriate for tests of mediation; therefore, longitudinal studies are essential for exploring the dynamics of critical consciousness development. Furthermore, a person‐centered approach (e.g., Byrd & Ahn, School contexts are complex, and research should do more to consider how youth negotiate messages from multiple sources. Work on familial socialization may serve as a guide. For instance, A study of African American and Latinx adolescents found that congruent cultural socialization messages from family and peers resulted in better socioemotional and academic outcomes (Wang et al., In conclusion, this study has indicated that negative interracial interactions and opportunities to learn about structural racism can help youth to understand structural racism and promote critical action. Researchers and educators should work to understand the ways in which all dimensions of critical consciousness can be fostered in adolescents. Dismantling oppression can begin with resistance in the classroom: through teachers raising students' awareness of histories of racism and the ways in which racism occurs in their own school setting. For many youth, this increased awareness will prompt them to take on oppression as it manifests in their individual interactions, their communities, and society at large.


Late access to antenatal care is a contributor to excess mortality and morbidity among ethnic minority mothers compared to White British in the UK. While individual ethnicity and socioeconomic disadvantage are linked to late antenatal care initiation, studies have seldom explored patterns of late initiation and associated factors in ethnically dense socially disadvantaged settings. This study investigated disparities in the timing of antenatal care initiation, and associated factors in an ethnically dense socially disadvantaged maternal cohort.N = 46,307). Late initiation was defined as first antenatal appointment attended at > 12 weeks of gestation and further classified into moderately late (13–19 weeks) and extremely late initiation (≥ 20 weeks). We applied logistic and multinomial models to examine associations of late initiation with maternal and sociodemographic factors.A retrospective cross-sectional study using routinely collected anonymous data on all births between April 2007—March 2016 in Luton and Dunstable hospital, UK  started antenatal care at > 12 weeks of gestation. Prevalence of late initiation varied across ethnic groups, from 16.3% (White British) to 34.2% (Black African). Late initiation was strongly associated with non-White British ethnicity. Compared to White British mothers, the odds of late initiation and relative risk of extremely late initiation were highest for Black African mothers [adjusted OR = 3.37  for late initiation and RRR = 4.03  for extremely late initiation]. The odds did not increase with increasing area deprivation, but the relative risk of moderately late initiation increased in the most deprived ] and second most deprived areas [RRR = 1.23 ]. Late initiation was associated with younger mothers and to a lesser extent, older mothers aged > 35 years. Mothers who smoked during pregnancy were at higher odds of late initiation compared to mothers who did not smoke.There is a need to intensify universal and targeted programmes/services to support mothers in ethnically dense socially disadvantaged areas to start antenatal care on time. Local variations in ethnic diversity and levels of social disadvantage are essential aspects to consider while planning services and programmes to ensure equity in maternity care provision. Antenatal care, defined as the care provided to an expectant mother by health care professionals from the time the pregnancy is confirmed until the onset of labour, has long been recognised as an effective way of maximising positive birth outcomes for pregnant women and their babies. A number of national and international guidelines have highlighted the importance of timely initiation of antenatal care –6. In thWomen from Black and minority ethnic backgrounds are found to be at greater risk of initiating antenatal care later than recommended gestational week compared to White women in the UK as elsewhere –16. SystLate and differential access to antenatal care has also been posited as a plausible explanatory factor for excess mortality and severe morbidity and among mothers from ethnic minority groups compared to White British women in the UK . For exaSimilar to ethnic minority status, maternal socioeconomic deprivation has frequently been cited as a predictor to delayed or inadequate antenatal care access –23. FeijIt has been argued that individuals living in ‘ethnically dense’ areas with members of their own group tend to enjoy better health compared to their counterparts in less ethnically diverse areas . While ith most deprived area from 317 local authorities in England as defined by the nationally derived Indices of Multiple Deprivation (IMD) scores  and increased after adjusting for other factors [adjusted OR = 3.37 ]. For extremely late initiation (20 + weeks), the relative risk was also highest for Black African mothers [RRR = 2.66 ] and the increased odds persisted after the adjustment. For South Asian mothers including Indian, Pakistani and Bangladeshi, the excess odds of late initiation (vs White British mothers) ranged from 24% among Indian mothers [OR = 1.24 ] to 32% among Pakistani mothers [OR = 1.32 ]. The excess odds doubled among Pakistani mothers [adjusted OR = 1.59 ] and Indian mothers [adjusted OR = 1.69 ]. For extremely late initiation, the relative risk was lower among Bangladeshi mothers compared to White British mothers [RRR = 0.78 ]. However, the relative risk increased and was borderline significant after adjustment [adjusted RRR = 1.20 ]. For White Other group, the odds of late initiation was also higher [OR = 2.07 ] and increased after the adjustment [adjusted OR = 2.31 ]. For mothers with data on country of birth, the strength of the association for all ethnic groups weakened after adjusting for migrant status, except for Indian and Pakistani mothers where there was a small increase in the odds of late initiation ] and the second most deprived areas [RRR = 1.23 ], compared with the least derived area. The association disappeared after adjusting for other factors. On the contrary, the relative risk of extreme late initiation was lower in the top two deprived quintiles both before and after the adjustment ] and to a lesser extent, older mothers (> 35 years) [OR = 1.19 ] compared to those aged 26–35 years. The odds changed little after adjustment for younger mothers, but for older mothers the odds were no longer significant after adjustment.p < 0.05).Mothers who smoked during pregnancy was more likely to start antenatal care late , but the association was no longer significant after the adjustment. Women with three or more previous births were more likely to have late initiation  and the association persisted after the adjustment. For extremely late initiation, the relative risk was lower among mothers with three or more previous births [RRR = 0.87 ], but it increased after the adjustment [adjusted RRR = 1.16 ]. Mothers who were never married had higher odds of late initiation [OR = 1.69 ] compared to married mothers and the odds ratio changed little after the adjustment. Mothers who were divorced/separated had a modest increase in odds of late initiation [OR = 1.20 ]. Among mothers with information on country of birth data, those who were born aboard were at 65% higher odds of late initiation compared to mothers born in the UK  or older (> 35 years), as well as being primiparous, possibly because women with no experience of pregnancy were less confident to navigate maternity services or may be unaware about the need to initiate antenatal care early in pregnancy. While older women may feel more confident to navigate the services, they may not feel the need to initiate antenatal care early in pregnancy. Unfavourable previous experiences with maternity or health services in general could also deter women from accessing antenatal services. Nationally, higher proportions of pregnant women aged < 25 years attended the booking appointment at a later stage than older women . In our This is one of the few studies that used a large recent data set to examine predictors of antenatal care initiation in an ethnically dense area with high levels of deprivation. Our cohort had substantially higher proportions (34.8%) of mothers from ethnic minority groups compared with the maternal population in England and Wales (12.9%) allowing greater statistical power to explore differences by ethnic group. The ability to analyse data at a local level providing opportunities to inform local policy and practice was also a strength of this study. Missing data on country of birth limited the ability to understand the predictors of late initiation of antenatal care at a more granular level, however. Data regarding languages spoken, education levels, father’s country of birth and migrant status would have allowed a greater depth of analysis when looking at factors associated with initiation of antenatal care among this cohort in order to inform the development of targeted interventions. The cross-sectional nature of the study may have limited causal inferences. Few ethnic minority mothers in our sample were from less deprived areas and this affected our ability to compare ethnic differences by the level of area deprivation.Compared to the national average, a high prevalence of late antenatal care initiation in our maternal cohort from an ethnically dense area with high levels of social disadvantage has implications for maternity care policy and practice locally, nationally and internationally. With increasing global migration, ethnically dense communities in deprived neighbourhoods are becoming commonplace in many developed countries. Mothers in these communities could potentially be at higher risk of falling behind in terms of their access to maternity services compared with the nation as a whole. The prevalence of late initiation was higher among minority groups including other White, and was particularly high among Black African and Caribbean mothers. The findings also highlight the differences in health care seeking behaviours and the relative inequity in health care access that tend to exist in countries such as the UK where healthcare is considered to be of universal access, accessed free of cost at the point of delivery.There has been a drive to increase the numbers of women initiating antenatal care in the first 10 weeks of pregnancy through initiatives such as direct self-referrals to local midwifery services in the UK . While tFuture research should include individual indicators of SES in addition to area deprivation to capture the impact of both individual-level and area-level SES on variations in antenatal care initiation. Development and evaluation of effective interventions, co-produced with women, families and maternity care professionals, taking into account cultural beliefs and practices to encourage and support women from ethnic minority groups to engage with maternity services is another area for future research.


Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex , they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm ; and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data‐driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision‐making for patients with intractable epilepsy. In this study, we use hidden Markov modeling (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric epilepsy patients. This data‐driven model produces an output unique to each patient, and is used to localize the epileptogenic area(s). In two patients, where more than one focus is identified, the HMM provides additional information about the relationship between the epileptiform activity arising in those areas. This may be augmented by the nuclear medicine techniques of positron emission tomography and especially ictal (during seizure) single photon emission computed tomography. If candidate epileptogenic locations are identified via imaging, a more invasive intracranial EEG (iEEG) may be initiated where electrodes are located on the brain surface or within the grey matter. This allows electrophysiological assessment with optimal sensitivity and spatial accuracy, prior to resective surgery. Despite this extensive surgical planning, fewer than 50% of patients are seizure free 5 years postsurgery, with this number dropping to 38% at 10 years postsurgery  measures the magnetic fields induced by neuronal current flow. Unlike the electric potentials measured by EEG, magnetic fields are relatively unaffected by the high resistivity of the skull, resulting in less spatial distortion of the MEG compared with the EEG signal, and thus improved resolution. MEG is used in a growing number of clinical settings, particularly in epilepsy. Not only does MEG provide additional information about the location of the epileptogenic zone , in others it differs on each occurrence. This makes automatic detection algorithms challenging to design.There are two commonly used analysis methods for localization of interictal activity, equivalent current dipole (ECD) fitting and excess kurtosis mapping (EKM). In ECD, interictal spikes are inspected visually  has gained traction in recent years as a method to elucidate complex neural dynamics in MEG data . All research subjects were scanned as part of clinical care at the Children's Hospital of Philadelphia. Data from 10 pediatric patients undergoing presurgical epilepsy evaluation between the ages of 11 months and 17 years  to allow continuous localization of their position relative to the MEG sensor array. All MEG scans were followed by an anatomical MRI during which MRI contrast markers were placed at the same fiducial points on the head. Coregistration of the MEG and MRI fiducial locations thus enabled complete spatial mapping of the MEG array relative to individual brain anatomy. This coregistration, in turn, allowed generation of functional images showing the cortical origins of epileptiform activity.Given that the purpose of this work is to act as a feasibility study to test whether the HMM could be a useful tool in the identification and localization of epileptiform activity, patients were selected retrospectively to fall into three groups of increasing localization difficulty . A comparison with scalp EEG is also given in Table 2.2Following MEG collection, data were visually inspected and any 2‐min runs containing obvious interference or segments where patient motion exceeded 1 cm were removed from further analysis. Since some patients have infrequent interictal activity, all data were checked by a MEG expert (William Gaetz) to ensure they contained epileptiform activity and runs which did not were subsequently discarded. This left an average of 11 runs per subject. Our HMM‐based method of mapping epileptiform activity comprised a two‐step process . An observation model links the HMM state to the observed values in the MEG data.To find spatiotemporal patterns corresponding to epileptogenic activity, we applied a multivariate, five‐state, time delay embedded HMM, in channel space. An HMM assumes that a series of recurring mutually exclusive “hidden” states govern the MEG data, such that every point in time is associated with one of the states. The sequence is assumed to be Markovian  were bandpass filtered between 20 and 70 Hz , notch filtered at 60 Hz (to remove mains frequency artifacts), temporally down sampled to 150 Hz, time‐embedded using 73 ms lags and a principal component analysis was used to reduce the data to 50 components . A total of fifty principal components were enough to capture 87% ± 3% of the data variance (mean and SD over subjects and runs). Note that each 2‐min clinical run was considered separately, just as the EKM data were. The model inference itself was undertaken using a variational Bayesian method which seeks to minimize the free energy of the system. We computed five‐states; for each state, in addition to an observation model, we obtained a time course of the probability that the state is active. These time courses were thresholded at two thirds, so the state was defined as “active” when the probability exceeded two thirds  and for various numbers of states (ranging from 3 to 9). Please see the Supplementary Information 2.2.2Following application of the HMM (in sensor space), a linearly constrained minimum variance beamformer . Those states whose time courses showed epileptiform activity when the state was active were identified, and the spatial localization was noted for each run. These were termed the “epileptiform state(s).”2.2.3We compared the results of our HMM, to the more established EKM technique. We selected EKM for this comparison because of its advantages over ECD  implementation, the brain was divided into a regular grid of 5 mm voxels and a scalar beamformer  was used to reconstruct electrophysiological signals at each voxel. A kurtosis value was then computed for the time course at every voxel, and voxels with a kurtosis value > 0.5 were marked as peaks  were compared with peak locations in kurtosis. We measured Euclidean distance between these peaks.Temporal coincidence: We took all of the time points identified by our EKM pipeline as containing epileptic activity and determined the number which were temporally coincident with an occurrence of the epileptiform state. Here, temporal coincidence was defined as within 73 ms of the EKM marker . Note that, the addition of a small amount of time around the EKM‐derived marker can mean that a single EKM marker is matched by more than one occurrence of the HMM epileptiform state; consequently, counterintuitively, values can exceed 100%.Following EKM, two separate measures were derived to quantitatively compare the output of the HMM and EKM mapping:All of the above  were applied to each 2‐min run, in each subject, separately. This meant 123 runs in total over 10 subjects.https://github.com/ZSeedat/HMM_epilepsy.The code used for model inference, computation of state‐wise functional maps, and comparison of the HMM method with EKM is available at 3Case study results for each of the three patient groups are shown here. For detailed analysis of all patient rs, please see the Supplementary Information 3.1Results for a single representative focal epilepsy patient are shown in Figure The patient exhibited abundant, large amplitude spikes, with some slow wave activity. Spatial correspondence between EKM and HMM was 6 ± 2 mm (mean and standard deviation over eight 2‐min runs) and 102 ± 8% of EKM‐identified epileptic events were matched in time by the occurrence of the epileptiform state. Note that more than one state visit to a single kurtosis marker will occasionally yield values >100%. Across all datasets, the HMM epileptiform state was active for 4% ± 2% of the time.iEEG and postsurgical outcomes were available for this patient, with grids over the right lateral frontal, right orbitofrontal, and right interhemispheric fissure, as well as with a depth electrode in the right lateral frontal region. Interictal activity was seen in the right lateral frontal grid and right frontal depth electrode but was also seen broadly in the other two grids. Seizures arose mainly from the right lateral frontal grid and depth electrode but also in the other regions almost simultaneously. The iEEG and MEG are therefore concordant, especially when considering the broad onset in the intracranial study and the MEG report. Although motor strip was involved, the patient had a right frontal lobectomy sparing motor strip which resulted in diagnosis of focal cortical dysplasia (FCD) type 2B. The patient was then seizure free for 2 years with recurrence in the motor region but has been seizure free on adjusted medication for 3 years since then. Figure Three further cases of patients with focal epilepsy with spike and wave are shown in Figures 3.2Case 2 Figure  shows MEiEEG for this patient was stereo‐EEG. Interictal activity arose mainly from the mid superior temporal gyrus (STG) electrode, as well as seizures, but there were rare interictal discharges in the angular gyrus as well. The patient had a focal resection of the posterior STG which resulted in pathology of FCD 2B. She has been seizure free since surgery (9 months) and has been able to reduce medication. The surgery did result in a conductive aphasia, but this has since improved. MEG was concordant in the post‐STG but also had some involvement in middle temporal gyrus (MTG) and right posterior insula which were not involved on stereo‐EEG. MEG localizations are shown in yellow and red markers  on the preoperative MRI alongside the resection zone on the postoperative MRI in Figure A second case of focal epilepsy with polymorphic bursts is given in Figure 3.3The above results show relatively straightforward cases of focal epilepsy, where abnormal epileptiform activity arises from a single location in the brain. However, in more complex cases, abnormal activity can occur from more than one location (often simultaneously), and in such cases, the challenge becomes determining where these multiple regions are, how they are related, and if possible, which region serves as the driver of an epileptiform network causing other areas to exhibit epileptiform activity.Case 3 is a patient with multifocal epilepsy; in total 15 datasets were acquired in this individual, results from a single representative run are shown in Figure In this patient, iEEG was grids and strips over the right superior and inferior frontoparietal region and right subtemporal area. Interictal activity and seizures arose in the right superior frontoparietal grid with rare sharps in the inferior grid. Seizures had onset in the superior frontoparietal grid but also more broadly at times in the superior frontoparietal grid and a small area of the inferior parietal grid. The patient underwent a right frontal and anterior parietal resection which resulted in a left hemiparesis and visual field cut, but has been seizure free since surgery (5.5 years) and is now off medications. Pathology was also FCD 2B. MEG was concordant in the right perirolandic region but also showed something in the right occipital and right hippocampal tail that were not covered by the implant well enough to establish concordance. Preoperative and postoperative MRIs are shown in Figure 3.4Case 4 is another patient with multifocal polymorphic activity arising from both the left frontal and left temporal lobes. The HMM consistently separated the activity from these regions out into distinct states . However, success greatly depends on presurgical planning to accurately identify the epileptogenic region(s) and the current clinical pathway  is not always successful in identifying candidate regions for resection (or indeed implantation of the iEEG). Consequently, improvements to this pathway could enable more patients to become eligible for surgery and might offer improved outcomes for those who do have surgery.MEG has a significant and established role in epilepsy, offering high‐precision mapping of epileptogenic and eloquent cortex . Spatial agreement was within 8 ± 3 mm , showing that epileptic foci could be identified using the HMM with high spatial acuity. Temporal agreement was also good, with 94% ± 13%  of epileptiform events detected by the standard EKM pipeline matched by the occurrence of the epileptiform state, identified by the HMM. Importantly, whilst the HMM looks for a specific spatiospectral pattern, the EKM pipeline is only able to find the timing of epileptic bursts via the use of a threshold: specifically, the EKM threshold is set by some arbitrary parameter (6) multiplied by the RMS amplitude. Thus, epileptiform activity with lower amplitude is likely to be missed by this overly simplified technique. The HMM on the other hand relies on the occurrence of a specific multivariate spatiospectral pattern. Consequently, it is less likely to miss epileptiform bursts.The HMM has demonstrated advantages when looking at multifocal epilepsy cases. In such complex cases, ECD is limited since one must make an a priori decision on the number of active foci, which is always unknown. EKM is better, since it allows identification of multiple regions, and the manual comparison of kurtosis markers can identify whether one sharply contoured event from one location consistently precedes another. Similarly, the HMM can identify multiple epileptiform foci but provides more information regarding the temporal relationship between the identified regions. Specifically, where multiple foci appear within a single state, then those regions (by definition) are related temporally, that is, the regions are “active” together. This was seen in Patient 9 Figure  in whichSimilarly in Case 4 Figure , we discThe HMM method implemented in this study is designed to be a semiautomated spike‐detection algorithm to aid clinicians in quickly and efficiently identifying epileptiform discharges in temporal data as well as localizing the regions of the brain in which they arise. We anticipate that in the clinic, there will be two manual processing steps required: the first is the visual inspection of data to remove those datasets with excessive head motion. This could be easily undertaken by somebody without training in reading clinical MEG data or identifying epileptiform discharges and could potentially be automated (at least in part). The second step will be to interpret the output of the HMM to select the epileptiform state(s)—this will likely require the assistance of a trained epileptologist. This method utilizes both the power of machine learning (with the HMM) and clinical expertise, but there is the potential to further automate the pipeline by employing additional machine learning techniques to learn features of epileptiform states for automatic state selection in the future.There are a number of further extensions to the present methodology which should be considered. First, the current version of the model operates using sensor space data, and localization is achieved using a beamformer. It is theoretically possible to apply the beamformer first, and then the HMM, and this may offer advantages: For example, the beamformer is known to supress fields from non‐brain sources, which would mean a source space HMM is less likely to be biased by interference. However, it is also significantly more computationally demanding since the HMM must be run on many thousands of (voxel‐based) signals rather than a few hundred channels. This was found to be impractical with current computing power, and whilst brain parcellation  offers a means to reduce dimensionality, it also reduces spatial specificity which is of key importance in this application. Thus, we feel the method presented is the most practical currently, but the use of better computing may ultimately enable a source space HMM. In addition, although we have applied our method to MEG data, application to EEG data or concurrent EEG/MEG data would also be valuable. In the former case, the limitations surrounding the spatial resolution of EEG would mean low spatial specificity, but nevertheless, the HMM might offer a useful means to automatically identify time points which contain interictal events—this could enable a significant saving in time for epileptologists who review EEG data. For concurrent EEG/MEG, there is some evidence that the combination increases sensitivity  This may offer a significant advantage over MEG alone. It should also be noted that the data used in this study were from a single site and acquired using one type of MEG system (CTF)—future studies should test the reliability of the HMM optimized with these data at other sites.The advent of wearable MEG systems, based on optically pumped magnetometer detection of the neuromagnetic field  resulting in huge benefit to quality of life.Appendix S1 Supporting InformationClick here for additional data file.


Latent class models are increasingly used to estimate the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and are commonly fitted using Bayesian methods. These models allow us to account for ‘conditional dependence’ between two or more diagnostic tests, meaning that the results from tests are correlated even after conditioning on the person’s true disease status. The challenge is that it is not always clear to researchers whether conditional dependence exists between tests and whether it exists in all or just some latent classes. Despite the increasingly widespread use of latent class models to estimate diagnostic test accuracy, the impact of the conditional dependence structure chosen on the estimates of sensitivity and specificity remains poorly investigated.A simulation study and a reanalysis of a published case study are used to highlight the impact of the conditional dependence structure chosen on estimates of sensitivity and specificity. We describe and implement three latent class random-effect models with differing conditional dependence structures, as well as a conditional independence model and a model that assumes perfect test accuracy. We assess the bias and coverage of each model in estimating sensitivity and specificity across different data generating mechanisms.The findings highlight that assuming conditional independence between tests within a latent class, where conditional dependence exists, results in biased estimates of sensitivity and specificity and poor coverage. The simulations also reiterate the substantial bias in estimates of sensitivity and specificity when incorrectly assuming a reference test is perfect. The motivating example of tests for Melioidosis highlights these biases in practice with important differences found in estimated test accuracy under different model choices.We have illustrated that misspecification of the conditional dependence structure leads to biased estimates of sensitivity and specificity when there is a correlation between tests. Due to the minimal loss in precision seen by using a more general model, we recommend accounting for conditional dependence even if researchers are unsure of its presence or it is only expected at minimal levels.The online version contains supplementary material available at 10.1186/s12874-023-01873-0. Diagnostic tests are used widely to discriminate between individuals with and without certain conditions and diseases. The results of these tests have important consequences, for both decision on treatment of individuals and for population health interventions. As a result, accurate characterization of diagnostic tests is paramount for optimal decision making. The usefulness of a diagnostic test is a combination of its accuracy, namely sensitivity and specificity, as well as practical considerations including cost, ease of use and speed of results. Because of this variety of factors to consider, which can involve difficult tradeoffs, new tests are continually being developed that aim to improve upon previous tests in any of these factors. To truly compare test effectiveness we must be able to assess the accuracy of a diagnostic test with minimal bias and high precision.Standard methods estimate the sensitivity and specificity of a diagnostic test by comparing the results of a new test to the results of a ‘gold standard’ reference test. On the assumption that the reference test is indeed a ‘gold-standard’ or perfect test, with 100% sensitivity and 100% specificity, we can be certain of the true infection status of each individual tested and we can estimate the sensitivity and specificity of the new test directly. However, diagnostic tests are rarely perfect and in some instances there is no gold-standard test with which to compare. Examples of pathogens and infections where this is the case include Tuberculosis  and Inflconditional independence throughout the rest of this paper. It has been highlighted by several researchers [To estimate diagnostic test accuracy with data from multiple imperfect tests using LCM requires making assumptions. Simple LCMs make the assumption that, conditional on the true infection status, results from multiple tests on an individual are independent. That is, the result of one test provides no information about the result of another test given the infection status of an individual. We refer to this situation as earchers , 10 thatearchers . The assearchers  or randoearchers  into theearchers .In a simulation study by Wang et al.  the authMuch of the literature to date has focused on the importance of accounting for conditional dependence in disease positive individuals with much less discussion on the importance of conditional dependence among disease negative individuals. Above we discuss that disease severity or intensity may explain conditional dependence in disease positive individuals, In disease negative individuals, the presence of other parasites may work in a similar way, leading to a higher probability of false positive results on a range of tests, thus inducing a positive correlation among test results and a dependence between test results conditional on the true infection status .There has been little research on the impact on estimates of sensitivity and specificity of choosing to account for conditional dependence in disease positive or disease negative individuals only versus accounting for conditional dependence in both. Here we focus on LCM with random effects and carry out a simulation study investigating the size of bias and impact on precision of estimates of sensitivity and specificity and coverage of 95% credible intervals, when the conditional dependence structure is mis-specified. We also look at how the size of this bias changes depending on the level of dependence between tests. Finally, we extend the analysis from a motivating example that estimated the accuracy of five different diagnostic tests used in the identification of Melioidosis , to highWe begin this section with an overview of the latent class models used for diagnostic test accuracy before introducing our motivating example, followed by details of the simulation study utilizing the structured approach developed by Morris et al. .N individuals who all undergo R binary diagnostic tests. We have observed data Y = {yij; i = 1, …, N, j = 1, …, R} where yij represents the test result  of the jth test for the ith individual. We assume two disease classes, and we let di denote the true (but unobserved) infection status for individual i, with those who are truly infected having di = 1 and those who are truly not infected di = 0. The disease prevalence (i.e. the proportion for whom di = 1) in the underlying population is denoted π.We consider a sample of Se = Pr(y = 1| d = 1)) and the probability that an individual who is truly not infected will return a negative test result is defined as the specificity (Sp = Pr(y = 0| d = 0)). Each test j has its own sensitivity and specificity, denoted Sej and Spj. Under the assumption that the R diagnostic tests are conditionally independent, the likelihood of the observed data can be expressed as:For a given test, the probability that an individual who is truly infected will return a positive test result is defined as the sensitivity  and similarly the specificity as Spij = Pr. The likelihood in [Seij and Spij where we assume then that sensitivity takes the form:g(·) is a link function. In this study we use the inverse logit link, so g−1(x) = 1/(1 + ex−). αjd and βjd are unknown parameters to be estimated. βjd describes the dependency of test j in disease class d on the random effects such that if all βjd = 0, there is no dependence on the random effect and all j tests among both disease classes are conditionally independent. We can estimate the mean or median sensitivity and specificity of a given test from the two parameters αjd and βjd. The random effect sid is assumed to follow a standard normal distribution ) . For a more detailed description of random-effect latent class models see references [To account for conditional dependence between tests in either or both truly infected individuals or truly not infected individuals, we allow the sensitivity and/or the specificity to vary by individual using a random effect. This reflects the situation where some subject-specific characteristic, besides the true disease status of the individual, affects the test result seen. The subject-specific value of the ferences , 12 and Burkholderia pseudomallei. The data are from a cohort of 320 febrile adult patients recruited over a 6 month period from a hospital in the northeast of Thailand in 2004 [We illustrate the impact of different conditional dependence structures on estimates of sensitivity and specificity using data from a study that utilised LCM to estimate the sensitivity and specificity of five different diagnostic tests used in the diagnosis of Melioidosis . Melioid in 2004 . The fivIn the original analysis, Limmathurotsakul et al. implemented four different LCM with various conditional dependence structures as well as an analysis which assumed culture was a perfect gold standard. The LCM models varied from a model assuming conditional independence between all tests (Model 0) to those considering conditional dependence between a single pair of serological tests using fixed effects Models 1 and 2) and finally those that use random effects to represent dependence between all serological tests within a disease class (Models 3 and 4) but they did not consider a model that simultaneously accounted for conditional dependence within both true positive and true negative individuals. See Table  and 2 anTo evaluate the impact of mis-specifying the conditional dependence structure in latent class analysis on bias, coverage, and precision of estimates of sensitivity and specificity.j = 2, 3, 4, 5), of a similar nature to the serological tests in the motivating example, that exhibit different conditional dependence structures among themselves, and one test (j = 1), of a similar nature to a culture test, which is assumed independent of the serological tests. We consider four scenarios for the conditional dependence structure between serological tests described in Table π = 0.5). All tests have a median sensitivity of 0.65 (g−1(αj1) = 0.65) while the median specificity of the four serological type tests is 0.9 (g−1(αj0) = 0.9, j = 2, .., 5) and the median specificity of our independent reference culture type test is 0.99 (g−1(α01) = 0.99).Data are simulated on 500 individuals for five diagnostic tests. As in our motivating example, we imagine four tests j = , 3, 4, 5βjd equal to 1. When the median sensitivity is 65%, the inclusion of this random effect means the interquartile range for sensitivity is 48–78% and with a median specificity of 90% the interquartile range is 82–94%. In a secondary simulation, we also compared this scenario with two additional scenarios under different values for β , where lower values of the standard deviation correspond to a narrower interquartile range around the median sensitivity and specificity.For the three scenarios in which there is conditional dependence, we set j = 1, .., 5) for the median individual (with random effect 0) and the associated 95% credible interval.In each simulated data set we estimate the sensitivity and specificity of each diagnostic test  is assumed perfect, i.e. a gold standard model (GS) (Se = Sp = 1)CIndep Model: A conditionally independent (CIndep) model where test 1 (culture) is assumed imperfect  individuals only  and all tests  are assumed imperfect  individuals only  and all tests  are assumed imperfect  individuals  and all tests  are assumed imperfect , precision of those estimates measured by the empirical standard error, and the coverage of the 95% credible intervals. These measures are defined in Supplementary Table recorded , 20. Anyjlower = 1 − Spj, j = 1, . . , 5, and 1. This ensures that the probability of a positive test is higher for somebody with disease than without. In these same models, the specificity of our independent test (j = 1) is assumed to follow a beta prior distribution and the specificity of all other tests  is assumed to follow a beta prior distribution. Although in this simulation we are assuming test 1 is a culture test and therefore we could assume a much stronger prior distribution for specificity, for the purposes of a more generalizable simulation we have kept this relatively uninformative. Assuming a beta distribution corresponds to an assumption of 95% probability of the specificity being above 74% and a beta distribution corresponds to an assumption of 95% probability of the specificity being above 55%. Where we account for conditional dependence between tests using random effects, βjd is assumed to follow a gamma prior distribution. In this paper we assume that β is the same between all serological type tests  but that that the culture type test (j = 1) is independent, and for simplicity, we consider the case where βjd = βd, j = 2, . . , 5. The effect of this is that a change in the random effect si will cause the sensitivity and of all serological type tests for the ith individual to change by the same amount and similarly, the specificity of all serological type tests for the ith individual to change by the same amount. We implement all models in R [https://github.com/shk313/Evaluating-sensitivity-and-specificity-from-LCM-a-simulation-study.git.All models are fitted using Bayesian methods and so prior distributions must be specified for all parameters. In all models, the prior distribution for prevalence is assumed uniform between 0 and 1. In models where culture is allowed to be imperfect  the sensitivity of all tests are assumed uniform between a lower limit of 1 minus the specificity Seels in R , 22 usinels in R  and all j = 2, .., 5) are combined. Figure Throughout the presentation of the results tests 2–5 , when the model used assumes conditional independence between tests (CIndep Model) or conditional dependence between tests in negative individuals only (CDN Model), culture specificity is underestimated but serological tests specificity estimates are approximately unbiased. For specificity all scenarios had a Monte Carlo standard error less than 0.01 and, like with sensitivity, only six scenarios had a 95% Monte Carlo confidence interval for the estimate that contained zero.j = 2, .., 5). On the other hand, when there exists conditional dependence between tests j = 2, . . , 5 in disease negative individuals only, the coverage in those models that do not account for dependence remains close to 95% for serology tests but is below 85% for culture. The coverage of sensitivity for culture from the CDPN model is higher than the nominal 95% levels for both the CDN and CDPN data generating mechanisms with the upper limits of the confidence intervals approaching 100%.Tables j = 2, .., 5). For culture (j = 1), coverage is higher than the nominal 95% level for all models except the GS model under CIndep and CDN data generating mechanisms. In the CDP and CDPN data generating mechanisms there is good coverage with models that account for the conditional dependence of tests in disease positive individuals (CDP and CDPN models) and poor coverage (< 15%) with models that do not account for conditional dependence of tests in disease positive individuals .Specificity estimates for all models, except the GS model, show good coverage for serology tests  but differed for our independent culture test (j = 1). For culture, the empirical standard error of estimates for both estimands was larger using the most general model, CDPN model, similar across CIndep, CDP and CDN models, and 0 for the GS model which assumes the test was perfect. The loss of efficiency from using the most general model (CDPN model) was high for estimating the accuracy of culture but low for estimating the accuracy of serological tests. However, if we just consider using the CDP model  the loss of efficiency from using this model when the true data generating mechanism is CIndep was never more than 2% for either estimand and all tests.A complete table of precision estimates for each estimand within each scenario and for each model can be found in Supplementary Tables All results so far considered the scenario where the standard deviation for the random effect is equal to one. We also considered, in a secondary simulation, the bias in estimates of sensitivity and specificity at two other levels of the standard deviation for the scenario where there exists conditional dependence in serological tests among infected individuals (CDP). These results are shown in Fig. All simulations in GS, CIndep, CDP and CDN models passed our convergence checks and had a reported rank normalised split- We re-analysed the data used by Limmathurotsakul et al.  and exteTable We carried out a simulation study investigating the bias and coverage of sensitivity and specificity estimates arising from mis-specification of the conditional dependence structure in latent class models. We found that assuming conditional independence among tests within disease positive or disease negative individuals when conditional dependence exists leads to bias and poor coverage in estimates of test accuracy. Due to the minimal loss in precision seen by using a model which accounts for conditional dependence between serology type tests in disease positive individuals, our results suggest it makes sense to account for conditional dependence in positive individuals even if researchers are unsure of its presence or if it is only expected at minimal levels. And, if there is a suggestion that there is dependence in both disease positive and disease negative individuals we would recommend using the most general model, particularly if the specificity of diagnostic tests being investigated are less than perfect. The results from this simulation also reiterate findings from previous studies , 14, 16 Our simulation study revealed that the size of bias in estimates of sensitivity and specificity was greatest when there existed conditional dependence among disease positive individuals and latent class models used assumed conditional independence among disease positive individuals. The size of this bias increased as the standard deviation of the random effects increased. Bias was larger when conditional dependence existed among disease positive individuals than conditional dependence in disease negative individuals. This reflects the fact that the true specificity was reasonably high in our simulation at 90% compared to a moderate sensitivity of 65%. In similar scenarios, where specificity is generally believed to be higher than sensitivity, these findings highlight that considering dependence among the disease positive individuals is most important to reduce the bias in accuracy estimates.In the motivating example, accounting for conditional dependence in only disease positive or disease negative individuals may have resulted in biased estimates of the sensitivity and specificity of tests included in this analysis. Comparing the model that only considered dependence in positive individuals and the model that considered dependence in both positive and negative individuals, the median specificity of one test differed by 9 percentage points. Although dependence among disease negative individuals was thought to be negligible, examining the predicted frequencies for each profile highlighted shortfalls in the final selected model which assumed independence among tests in these individuals. This was confirmed with a relatively novel model comparison tool that addresses shortfalls of earlier estimates such as AIC and DIC . This reThere are limitations to this simulation study. Practically, we saw a limitation in implementing the CDPN model where some simulations exhibited divergent transitions and others a split e et al. . We conse et al. .The impact of biased estimates of sensitivity and specificity is twofold. Firstly, a test whose accuracy is underestimated may not be used when it could be useful  and secondly, a test whose accuracy is overestimated may be used when more useful tests exist. Both outcomes ultimately result in negative consequences for individuals and societies, so minimizing the bias in our estimates of diagnostic test accuracy is paramount. This paper serves to highlight that not only should conditional dependence be taken account of but that the choice of conditional dependence structure is important and should be considered in any analysis of diagnostic test accuracy that utilizes latent class models.Additional file 1.Additional file 2.Additional file 3.Additional file 4.


Lipid bilayer membranes are ubiquitous in natural chemical conversions. They enable self‐assembly and compartmentalization of reaction partners and it becomes increasingly evident that a thorough fundamental understanding of these concepts is highly desirable for chemical reactions and solar energy conversion with artificial systems. This minireview focusses on selected case studies from recent years, most of which were inspired by either membrane‐facilitated light harvesting or respective charge transfer. The main focus is on highly biomimetic liposomes with artificial chromophores, and some cases for polymer‐membranes will be made. Furthermore, we categorized these studies into energy transfer and electron transfer, with phospholipid vesicles, and polymer membranes for light‐driven reactions. Vesicles can be used for the compartmentalization of chemical reactions and self‐assembly of reaction partners. This minireview highlights recent advances in biomimetic and light‐induced energy conversions with liposomes and polymersomes, relevant in the context of energy transfer, electron transfer, photocatalysis and artificial photosynthesis. Upon ph3Light‐induced charge separation is a challenge in solar energy conversion, including artificial photosynthesis and photoredox catalysis. Oftentimes, productive charge separation is in competition with unproductive charge recombination. The following three strategies ensure the forward reaction. I) Repulsion directly after the charge transfer,et al. assembled positively charged liposome membrane surface with a negatively charged zinc porphyrin photosensitizer yielding an overall positively charged membrane surface.The repulsion strategy is especially easy to realize with liposomes. By choosing the lipid composition, the liposome surface charges can have positive, neutral or negative surface charges  and tetracyanoquinodimethane (TCNQ).[et al. mimicked mitochondrial transmembrane electron transport with liposomes containing reduced nicotinamide adenine dinucleotide (NADH) in their inner aqueous compartment. The encapsulated NADH was then oxidized by the membrane soluble TCNQ and in turn reduced ferricyanide located outside of liposomes.Electron transfer across the lipid bilayer membrane can be performed with membrane‐soluble electron relays such as hydrophobic quinones, 1‐methoxy‐e (TCNQ).43, 44light‐induced transmembrane electron transfer was achieved using DPPC phospholipid bilayer vesicles, functionalized with amphiphilic zinc porphyrins. The sacrificial hydrophilic electron donor EDTA was encapsulated within these vesicles, and upon irradiation with visible light the photoreduction of the porphyrin took place. Electron transfer across the membrane only took place when the electron relay 1‐methoxy‐N‐methylphenazium (MMP+) was added as methyl sulfate salt: reduction of MMP+ yielded the neutrally charged MMPH which is hydrophobic enough to cross the hydrophobic membrane. The water soluble 2‐(4‐iodophenyl)‐3‐(4‐nitrophenyl)‐5‐‐2H‐tetrazolium anion (WST1−) was added to the bulk and was reduced to the strongly colored formazan dye Fz12−. Interestingly, the quantum yield for Fz12− generation was 2.7 times higher when the direct photoreduction was physically separated from the Fz12− generation by the vesicle membrane  have proven to be very versatile in properties55, 56Polymersome membranes are generally constructed by self‐assembly of individual AB or ABC linear, branched or dendritic copolymers .As described earlier, polymersomes can be constructed of hydrophobic‐hydrophilic block‐copolymers. Typically, the A‐part composes of the hydrophilic part while the B‐part of the co‐polymers consists of the hydrophobic tail. An additional hydrophobic or hydrophilic C‐block‐copolymer might be added as well. To address the issue of mass transport from the inside to the outside of the polymersome, several approaches have been proposed. They include integration of transmembrane proteins,61, 62In summary, we have presented several recent studies on light induced electron and energy transfer within liposomal and polymer membranes that will be highly relevant for the construction of artificial photosynthetic membrane systems. We believe that the aspect of compartmentalization of reactions and self‐assembly of reactants will become more and more important for chemistry in general, and light energy conversion in particular. Using vesicles based on biomimetic lipids or artificial polymers will enable various strategies in this regard.The authors declare no conflict of interest.Novitasari Sinambela studied chemistry at the Bandung Institute of Technology, Indonesia. She received her MSc degree in 2019. She is currently a PhD candidate in the laboratory of Andrea Pannwitz at Ulm University. Her work focusses on liposomes for artificial photosynthetic purposes, such as photocatalytic water splitting and transmembrane electron transfer.Julian Bösking studied chemistry at Ulm University, Germany. After receiving his MSc degree in 2020 he started his PhD at Ulm University. His work focusses on energy transfer in vesicles.Amir Abbas studied chemistry at the University of Sassari, Italy. He received his MSc degree in 2020 and is currently a PhD candidate in Andrea Pannwitz’ research group at Ulm University on the functionalization of liposomes and polymersome vesicles.Andrea Pannwitz obtained her PhD in 2017 at the University of Basel in Switzerland. After her Postdoc at Leiden University, The Netherlands, she started her independent career as junior professor for Inorganic Chemistry and Energy Conversion at Ulm University in Germany, with a major interest in photochemical conversions at membrane interfaces.


R2 = .87−.91), and a linear response to annual precipitation (adjusted R2 = .82). Mean annual temperature and annual precipitation in the second‐order polynomial model together explained 96.3%−98.7% of the variation in species richness. The variation in species richness uniquely explained by minimum temperature of the coldest month was much greater than that uniquely explained by temperature seasonality, but the variation in species richness uniquely explained by precipitation during the driest month was much smaller than that uniquely explained by precipitation seasonality. Overall, extreme climate variables explained slightly more variation than did climate seasonality. Our study suggests that pteridophyte richness along the elevational gradient is largely driven by a combination of both temperature‐ and precipitation‐related parameters, although precipitation‐related variables play a slightly stronger role, and that extreme low temperature events (at high elevations) and seasonal precipitation variability (at low elevations) are the strongest determinants of pteridophyte species richness.There is a consensus that climate factors strongly influence species richness along elevation gradients, but which factors are crucial and how they operate are still elusive. Here, we assess the relative importance of temperature‐related versus precipitation‐related variables and the relative importance of extreme climate versus climate seasonality in driving pteridophyte species richness. We used correlation and regression analyses to relate species richness of pteridophytes, and their two major groups , in fifty 100‐m vertical bands to climatic factors representing different aspects of climatic conditions . Variation partitioning analysis was used to determine the relative importance of each group of climatic factors on species richness. Across the entire elevational gradient, species richness had a parabolic response to mean annual temperature (adjusted  10‐tranformed area (km2) of the elevational band.Relationships between elevation and species richness (a) or species density (b) for pteridophytes in Nepal. Each dot represents a 100‐m elevation band. Species density is defined as species richness in an elevational band being divided by the log Species diversity generally varies between areas with different climatic conditions . Second, are climate extreme variables  more important drivers of pteridophyte species richness than are climate seasonality variables ? Considering that previous studies have shown that minimum temperature is more strongly related to distributions and richness of seed plants, compared with temperature seasonality  to be the best model  for statistical analyses.We used SYSTAT  but was strongly associated with annual precipitation (R2adj = .81−.82) , which was positively correlated with pteridophyte species richness Figure . Across 2, which When the lower and upper segments of the elevational gradient were considered separately, the amount of the variation in species richness explained by each of the six climatic variables did not differ greatly between the first‐order and the second‐order polynomial models, regardless of whether the lower or upper segment of the elevational gradient was considered and which group of species was considered Table . For exa3.3When species richness was simultaneously regressed on mean annual temperature and annual precipitation in the second‐order polynomial model, the two climatic variables explained 96.3%−98.7% of the variation in species richness for the three groups of species, with mean annual temperature uniquely explaining slightly more variation than annual precipitation for pteridophytes as a whole and for ferns Figure . HoweverWhen species richness was simultaneously regressed on the two variables representing extreme climate conditions  and two variables representing climate seasonality , the four climatic variables explained 95.2%−99.4% of the variation in species richness for the three groups of species, with 80.6%−81.7% of the variation being explained jointly by extreme climate and seasonality variables Figure . ExtremeWhen the variation in species richness explained by each full model was partitioned according to climate variables representing general conditions, climate variables representing extreme conditions, and climate variables representing seasonality, about 82% of the variation was explained jointly by the three types of climatic variables for each of the three species groups Figure . For all3.4When species richness of each of the three species groups  for the full elevational gradient, or its lower or upper segment, was regressed on the six climatic variables in a polynomial model for all possible combinations of the 12 model terms , a total of 4095 models resulted, of which the one with the smallest AICc was considered the best model. Of the nine best models for the three species groups and three types of elevational gradients , each model explained 96.5% or more variation in species richness and included 4 to 8 model terms  and its quadratic term were the two strongest factors, followed by minimum temperature of the coldest month (bio6), in the best models for pteridophytes as a whole and for ferns, based on the absolute values of standardized coefficients of the model terms Table ; annual When the lower and upper segments of the elevational gradient were considered separately, temperature seasonality (bio4) and its quadratic term had the strongest effects for pteridophytes in both lower and upper segments of the elevational gradient and for ferns in the lower elevational segment Table ; minimum4In this study, we used current data on the taxonomy and elevational distribution of pteridophytes in Nepal in combination with novel climate data to update and expand our understanding of the drivers of pteridophyte richness in the Himalaya, compared to the previous study of Bhattarai et al. . The maiThe hump‐shaped richness pattern observed in this study for pteridophytes along the elevational gradient in Nepal is similar to that observed in Bhattarai et al. . However2 ; Data curation (lead); Formal analysis (lead); Investigation (lead); Writing – original draft (lead); Writing – review & editing (lead). Michael Kessler: Writing – original draft ; Writing – review & editing . Ole Reidar Vetaas: Writing – original draft (supporting); Writing – review & editing .The authors declare no conflict of interest.Table S1Click here for additional data file.


This study investigated whether men with a history of real‐life aggressive, dominant behavior show increases in testosterone and cortisol levels after brief social contact with women. Furthermore, we tested the prediction that such changes in hormones would be larger than those observed previously in young male students. Sixty‐seven male participants convicted of intimate partner violence (IPV) either had brief social contact with a female confederate  or a male confederate (control condition). We also performed meta‐analyses to investigate whether IPV perpetrators' hormonal responses were larger than the typical responses of young male students in prior studies. All statistical analyses were preregistered. Change in testosterone did not differ across experimental conditions, and testosterone in the IPV perpetrators actually declined from baseline in the female confederate condition. Our meta‐analysis showed that this testosterone decrease was different from the testosterone increase typically observed in young male students. The cortisol levels of IPV perpetrators did not change in response to contact with women. This result was consistent with our meta‐analysis since young male students also did not experience a cortisol change in response to interactions with women. In sum, our findings provide no evidence that male IPV perpetrators exhibit larger hormone increases to brief interactions with women, although it is possible that the men in this sample did not perceive the social contact period as a courtship opportunity. These results suggest that hormone reactivity to social encounters may differ across subject populations and depend on how subjects perceive social situations within laboratory settings. Indeed, in young heterosexual male students, testosterone and cortisol levels increased after brief social contact with a potential mate, but not after contact with a man  perpetrators. The relationship between baseline testosterone and aggressive behavior in men is not especially strong, with meta‐analyses showing significant relationships but small overall effect sizes , and 33 men had contact with another man (control condition). Testosterone and cortisol levels were measured before and after the contact period. Furthermore, we used meta‐analytical techniques to compare effect sizes between our study and studies with young male students. All analyses were preregistered here 22.1M = 41, SD = 12, range = 21–76). They were recruited from a psychoeducational and community‐based treatment program (Contexto programme) of the University of Valencia . There were no significant differences between the two groups in age, educational level, alcohol consumption, and number of cigarettes smoked per day (see Table The participants were randomly assigned to one of the two conditions (contact with a woman: https://osf.io/2ntcu. These three criteria were: (i) medication that directly affects the secretagogues of the hypothalamic–pituitary–adrenal axis or hypothalamic–pituitary–gonadal axis, (ii) recovery from severe medical treatment , (iii) under the influence of drugs or alcohol during the study. However, we found that nine participants met a fourth exclusion criteria, as they probably took daily medication (exact frequency unspecified) that could affect cognitive, behavioral, or emotional responses (either antidepressants or benzodiazepines). However, excluding such a significant percentage of participants from our analyses would substantially reduce the power to detect an effect. Therefore, we chose to report our results with and without these participants and include this medication use as a covariate, see Table All the participants reported being heterosexual . None of the participants met any of three of the four criteria for exclusion as specified in the preregistration of our analyses, see here 2.2Contexto), they were informed that we were looking for volunteers for a study investigating the physiological changes during the performance of some tasks. To strengthen our cover story during the interaction with the stimulus person, we told them that we were looking for volunteers among all the people who come to the faculty. If they agreed to participate, the men would participate in our study before starting their psychoeducational treatment program.The experiment was carried out at the Faculty of Psychology . To recruit participants, during the first session of the psychoeducational and community‐based treatment program .The session started with 20 min of habituation in a quiet room (room A). During this phase, their height and weight were measured, and the participants were asked to complete a questionnaire. At the end of the habituation, the participants provided the first saliva sample (Sample 1) to measure their cortisol and testosterone levels. Participants were then brought into a different room (room B) where there was a confederate present who appeared to be another participant of the study. As part of the cover story, the experimenter informed the participant and the stimulus person that he had to make some extra copies of the questionnaires, and the participant and stimulus person were then asked to wait. The participant and the stimulus person were left alone to wait together for 10 min. After 10 min, the experimenter returned to room B and asked the stimulus person to leave the room. Then, the participant completed two questionnaires for 10 min. Finally, the participant was brought into room A, where he was asked to provide another salivary sample (Sample 2) and complete some questionnaires. The second sample was 20 min after the start of the conversation, which was the same as the other studies in the meta‐analyses  and was conducted in accordance with the Declaration of Helsinki.2.3M = 5.06, SD = 1.65) and male confederates were rated as below average on attractiveness  on a scale from 1 (not very attractive) to 7 (very attractive). The research assistant carrying out the study was male.Four men  and six women  played the role of the stimulus person. The confederates were researchers from our laboratory and did not have contact with the participant before the experiment. Following van der Meij et al.  and Rone2.4Salivary samples to assess cortisol and testosterone levels were collected by passive drool. Participants were asked to deposit 5 ml of saliva in plastic vials, and samples were frozen at −80°C. Biochemical analyses were done by the Laboratory of Social Cognitive Neuroscience . For cortisol levels, the samples were analyzed in duplicate with the salivary cortisol enzyme‐immunoassay kit from Salimetrics. Assay sensitivity was 0.007 μg/dl. For each subject, all the samples were analyzed in the same trial. The mean inter‐ and intra‐assay coefficients of variations were all below 10%. For testosterone levels, the samples were analyzed in duplicate using enzyme‐immunoassays with the expanded range salivary testosterone enzyme‐immunoassay kit from Salimetrics. Assay sensitivity was 1.0 pg/ml, and the mean inter‐ and intra‐assay coefficients of variation were all below 10%.2.5https://osf.io/2ntcu. To investigate Hypothesis 1, we used linear mixed models for repeated measures in SPSS . As the dependent variable, we included either the testosterone or cortisol levels. As predictors, we included Moment (pre or post) and Condition (contact with men or women). We selected an unstructured correlation metric as the covariance structure.The statistical analyses to test Hypothesis 1 were preregistered here Robustness statistical analyses Hypothesis 1: We also tested how robust statistical conclusions were when analyzing Hypothesis 1, see Table Furthermore, we tested how much confidence should be placed in our result using Bayesian analyses for paired samples. These analyses were performed using JASP version 0.14.1 with default prior scales, following van Doorn et al. .2.6https://osf.io/2ntcu. To test Hypothesis 2, we used meta‐analyses with the package Metafor , (ii) Roney et al. Study 1 , (iii) Roney et al. Study 2 , (iv) van der Meij et al. (n = 30), (v) Roney et al. (n = 90), see also the preregistration. We also added an unpublished study from the first author with the same methodology . The total sample size of the testosterone meta‐analysis was 315.The statistical analyses to test Hypothesis 2 were preregistered here j et al. , n = 30,y et al. , n = 90,n = 77), (ii) Roney et al. Study 2 , (iii) Roney et al. (n = 82). We also added an unpublished study from the first author (n = 21) and the study from van der Meij et al. (n = 40) with the same methodology . The total sample size of the cortisol meta‐analysis was 293.We also used the same meta‐analytic approach to compare if the cortisol change in the sample of IPV perpetrators was larger than the cortisol change in young male students in the following studies: (i) Roney et al. Study 1 . However, there was a main effect of Moment , which showed that testosterone levels decreased after social contact irrespective of the sex of the stimulus person (drm = 0.21). Although there was no significant interaction between Moment and Condition we did analyze the testosterone response separately by condition to explore potential trends in line with our hypothesis. These results showed that testosterone levels decreased after contact with women  and did not change after contact with men . Furthermore, there was a marginally significant effect of Condition , showing that the participants who had contact with women had overall lower testosterone levels than participants who had contact with men (d = 0.34). Adding covariates and removing outliers in the mixed model did not substantially change the statistical conclusions of the testosterone decrease in response to women, but when only using raw testosterone values, the change in testosterone was nonsignificant after contact with women ; see also the Supporting Information. Furthermore, Bayesian analyses showed strong evidence for the absence of an increase in testosterone levels (H0) in response to women (BF10 = 0.06) compared to an increase in testosterone levels (H1). Furthermore, there was moderate evidence (4.93 times more likely) for testosterone levels to decline after contact with women (H1) compared to testosterone levels not declining (H0). See Figure The mixed model with testosterone as the dependent variable showed that there was no interaction between Moment and Condition  and no main effect of Condition . However, there was a main effect of Moment , showing that cortisol levels decreased after social contact irrespective of the sex of the stimulus person (drm = 0.28). Although there was no significant interaction between Moment and Condition we did analyze the cortisol response separately by condition to explore potential trends in line with our hypothesis. These results showed that cortisol levels decreased after contact with men  and did not change after contact with women . Adding covariates, removing outliers, and only using raw cortisol values did not change the statistical conclusions of the cortisol decrease in response to women (see the Supporting Information). Furthermore, Bayesian analyses showed strong evidence for the absence of an increase in cortisol levels (H0) in response to women (BF10 = 0.09) compared to an increase in cortisol levels (H1). See Figure The mixed model with cortisol as dependent variable showed that there was no interaction between Moment and Condition . However, effect size variance could be explained by between study differences . Including the moderator showed that the testosterone change was smaller in IPV perpetrators than in young male students , see Figure Excluding data from the current study. Not including the data on the IPV perpetrators showed that testosterone levels increased in response to contact with women . Additionally, effect size variance could not be explained by between study differences . Inspection of the forest plot did not reveal any outliers. Evidence concerning the presence of a publication bias was mixed: there was no asymmetry in the funnel plot  but Duval and Tweedie's trim and fill approach revealed that two studies could be filled below the estimated effect size. Addition of these two studies resulted in a slightly smaller overall effect size .3.4All studies. The meta‐analysis showed that when including all studies there was no cortisol change in response to contact with a woman . However, effect size variance could be explained by between study differences . Including the moderator showed the cortisol change in IPV perpetrators was not different from young male students , see Figure Excluding data from the current study. Not including the data on the IPV perpetrators showed that cortisol levels did not change in response to contact with women . However, effect size variance could be explained by between study differences . Inspection of the forest plot did not reveal any outliers. There was also no evidence for publication bias as there was no asymmetry in the funnel plot  and Duval and Tweedie's trim and fill approach revealed that no studies could be filled below or above the estimated effect size.4Our results concerning testosterone did not confirm our predictions: testosterone responses did not differ across the female and male confederate conditions, and in the female condition specifically, the testosterone levels of IPV perpetrators declined from baseline. Our meta‐analysis also showed that this decrease was different from young male students as the latter typically showed an increase in testosterone levels in response to social contact with women. These results were surprising as we had hypothesized that perpetrators would respond with an even higher testosterone response than young male students. We based our prediction on previous findings showing that a more self‐reported aggressive dominant personality in young male students was related to a larger testosterone increase in response to brief social contact with women . Interestingly, some preliminary evidence shows that the cortisol response toward women is moderated by other factors, which could reduce the power to detect an effect. For example, men with more sexual experience showed higher testosterone responses in one study  had contact with a stimulus person who was matched on age and socioeconomic status, (ii) only waited and had no social contact, (iii) were not convicted of domestic violence. A final limitation is that relatively many of our participants used regular recreational drugs (slightly more participants who had contact with women) and antidepressants & benzodiazepines. This may cause hyperprolactinemia and indirectly affect the luteinizing hormone releasing (LNRH) axis, which could have influenced the participants' hormonal response. Although it may be difficult, including more intimate violence perpetrators without this drug use would improve result robustness. It would be a real challenge for future research to address all previously mentioned limitations as it would mean increasing the sample size substantially. In the current study, it was already complicated to get the current number of participants who had a history of domestic violence.In conclusion, this study did not find any evidence that male IPV perpetrators exhibit larger hormone responses to social interactions with women than other men, despite sound theoretical reasons to predict this possibility. Although it is possible that perpetrators might exhibit larger hormone responses under circumstances in which women interaction partners are more similar to them in age and socioeconomic status, our findings nonetheless argue against robust hormone reactions to interactions with women in general, even though the perpetrators rated the women interactants as above average in attractiveness. Concerning the hormone responses to social interactions among all men, our findings provide evidence that findings may importantly differ across different subject populations. Our meta‐analysis indicated that young male students exhibited larger testosterone responses to women than did this sample's IPV perpetrators, suggesting that the hormone responses may be moderated by variables such as viewing the interaction as a courtship opportunity or perhaps even by age itself. Such moderators could be more rigorously tested in future research on hormonal responses to interactions with potential mates.Supporting information.Click here for additional data file.


Increasing demands and changes in the living style due to overpopulation has increased the amount of waste generated globally. The waste management system must meet the criteria of the United Nations' sustainable development goals, including being cost‐effective, societally useful and environmentally safe   such as docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and docosapentaenoic acid (DPA)]  has been extensively investigated and usually accumulates lipids using a hydrophilic substrate such as glucose, while the external medium is depleted of macro‐elements (mainly nitrogen and phosphorous)  can grow and utilize lipid‐containing medium as a sole carbon source  play a major role in the metabolism of fatty acids and usually represent a rate‐limiting step with a different range of substrate specificity   emulsification (contact between the substrate and cells via surfactant or liposan emulsifier production) or adhesion of cells to hydrophobic substrate droplets, (2) Hydrolysis by expression of lipolytic enzymes and (3) Uptake of the free fatty acids using active transport inside the cells  should be used to understand the metabolic pathways and identify key enzymes and regulatory factors involved in the valorization of hydrophobic substrates. Using differential gene expression, omics analysis can provide critical information on physiological growth and other essential characteristics for future bioprocess studies. The second stage is to reconstruct the pathway using the rational alteration of fluxes to funnel down the desired products after their identification. The combination of insights into bioprocessing and molecular responses of cells, as well as the availability of various next‐generation genome modification techniques such as CRISPR/Cas9 and TALENs, has made it easier to develop novel single‐cell biorefineries with improved characteristics that can meet criteria for industrial use. EasycloneYALI is one such example which is a tool developed for streamlined strain improvement using CRISPR/Cas9 in the oleaginous yeast A. limacinum mh0186 were identified, cloned, and characterized using the comparative gene clustering method. This information was missing in the gene annotations due to no homology with existing lipases in the database  and ATP‐Citrate lyase (ACL)  in the external media  are the enzymes that can potentially affect the overall saturation index of the lipid produced by introducing double bonds (Cook & McMaster, T. oleaginous ATCC 20509. Also strains for higher PUFA accumulation as compared to the wild type were achieved through the expression of bi‐functional desaturase, elongase and ALA‐isomerase enzymes from different species into the yeast (Görner et al., Rhodosporidium toruloides (Tsai et al., A. limacinum mh0186 increased EPA by 4.6‐fold, while also some strategies were applied to express exogenous desaturases for changes in DHA production by Schizochytrium sp. (Kobayashi et al., Designing targeted tailoring of the fatty acid waste profile into appropriate value‐added lipids is a challenging process. Intracellular desaturases (as ΔMus musculus was over‐expressed in Lipomyces starkeyi for the production of long‐chain fatty alcohol production (McNeil & Stuart, Y. lipolytica (Cordova et al., Fatty alcohols are derived from the fatty acids generated and have importance in the industry as surfactants and emulsifiers. In a recent study, fatty acyl‐CoA reductase from Finally, it is critical to follow all applicable laws and regulations for the use of genetically modified organisms, which vary by nation, as the products may be used for nutraceuticals or pharmaceuticals.The amount of hydrophobic wastes produced will rise along with the population, and the current waste treatment techniques will not be adequate. Such wastes are potential substrates for their valorization into high‐value oleochemicals using oleaginous microbes. The industrial importance and market values of oleochemicals have drawn significant attention and are going to boost the industry in the next few decades due to their wide range of applications. It will be possible to explore thraustochytrids species at their full potential for large ranges of multi‐products through hydrophobic waste valorization considering recent advances in genetic engineering and bioprocessing in the future.The authors declare no conflict of interest.


Medical interventions that increase life expectancy of patients result in additional consumption of non‐medical goods and services in ‘added life years’. This paper focuses on the distributional consequences across socio‐economic groups of including these costs in cost effectiveness analysis. In that context, it also highlights the role of remaining quality of life and household economies of scale. Data from a Dutch household spending survey was used to estimate non‐medical consumption and household size by age and educational attainment. Estimates of non‐medical consumption and household size were combined with life tables to estimate what the impact of including non‐medical survivor costs would be on the incremental cost effectiveness ratio (ICER) of preventing a death at a certain age. Results show that including non‐medical survivor costs increases estimated ICERs most strongly when interventions are targeted at the higher educated. Adjusting for household size  and quality of life  mitigates this difference. Ignoring costs of non‐medical consumption in economic evaluations implicitly favors interventions targeted at the higher educated and thus potentially amplifies socio‐economic inequalities in health. WhetherMany countries adopting a societal perspective include production gains in economic evaluations but exclude costs of non‐medical consumption ISPOR, . This diThis paper estimates the distributional consequences of including non‐medical consumption costs in economic evaluations across groups with different socio‐economic status (SES). Doing so, we add an important element to the literature on cost‐effectiveness and inequality. There is a growing interest in distributional consequences in cost‐effectiveness evaluations, and many health policies are explicitly targeted at reducing inequalities in health  costs of non‐medical consumption are lower for individuals in multi‐person households than for singles, as the first group benefits from economies of scale ). Due to lower life expectancy of lower educated, they are at greater risk of living alone at older age relative to their higher educated counterparts  from the years 2003 and 2004 which is a yearly cross‐sectional survey collected among the non‐institutionalized population of the Netherlands. The households taking part in the survey report on a comprehensive set of consumption categories  using diaries which were collected by interviewers on a regular basis. We removed all medical consumption of the household and adjusted the data to 2017 prices. Age was defined as the age of the household head. Educational attainment was determined by the highest educational attainment of the household head and categorized in three categories: low, middle and high (for more details on the data and methods see the online Supplementary File). Figure 2.2Hh equiv. denotes annual non‐medical consumption per household equivalent. We used cubic P‐Splines to model the non‐linear age pattern with an interaction term for education and ε is a normally distributed error term ) are then calculated as in Equation nmThe first part denotes the consumption for individuals in households with more than one adult times the probability of a death being prevented in a multi‐person household. The second part represents the consumption for individuals in single households times the probability of a death being prevented in a single‐person household.2.3Equation  shows thΔnmcΔQALY from saving a life at different ages. These estimations may then be added to the ICER of a life prolonging intervention. ΔnmcΔQALY was calculated in the following manner:L is the number of years lived at age a for a particular educational group and Q is the average quality of life at age a for a particular educational group. Estimates of L, QoL were taken from a study that estimated the quality of life and mortality in the Netherlands stratified by education  gained manner:ΔnmcΔQALrelatively favorable for the interventions targeted at the low SES groups . Second, the higher educated enjoy both lower mortality rates as well as a higher quality of life resulting in a higher quality‐adjusted life expectancy at all ages  lower educated persons enjoy their added life‐years in a lower quality of life than higher educated, and (ii) lower educated persons more often form a single‐person household at an old age, which implies relatively high additional consumption costs compared to people in a multi‐person household. Especially this latter effect turned out to mitigate the socio‐economic differences induced by including the costs of non‐medical consumption. Finally, it needs to be noted that these influences also need to be viewed in relation to the inclusion of other costs (such as productivity costs) which may, on average, have opposite distributional effects.Although the impact of including costs of non‐medical consumption on the Our findings are in line with previous studies that investigated the costs of non‐medical consumption  which conducts contract research for public and private sector bodies. The institute is 100% owned by Erasmus University of Rotterdam and dr Versteegh does not receive any personal fees from collaborations with the private sector. Dr. Wouterse has nothing to disclose. Dr. van Baal has nothing to disclose.Supplementry Material S1Click here for additional data file.Supplementry Material S2Click here for additional data file.