Autism spectrum disorder (ASD) is a neurodevelopmental disorder that can cause significant social, communication, and behavioral challenges. Diagnosis of ASD is complicated and there is an urgent need to identify ASD-associated biomarkers and features to help automate diagnostics and develop predictive ASD models. The present study adopts a novel evolutionary algorithm, the conjunctive clause evolutionary algorithm (CCEA), to select features most significant for distinguishing individuals with and without ASD, and is able to accommodate datasets having a small number of samples with a large number of feature measurements. The dataset is unique and comprises both behavioral and neuroimaging measurements from a total of 28 children from 7 to 14 years old. Potential biomarker candidates identified include brain volume, area, cortical thickness, and mean curvature in specific regions around the cingulate cortex, frontal cortex, and temporal-parietal junction, as well as behavioral features associated with theory of mind. A separate machine learning classifier  was used to validate the CCEA feature selection and for ASD prediction. Study findings demonstrate how machine learning tools might help move the needle on improving diagnostic and predictive models of ASD. Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that can cause significant social, communication, and behavioral challenges. According to the most recent report from the Center for Disease Control and Prevention (CDC), the number of children in the U.S. diagnosed with ASD are about 1 in every 54 [ASD is characterized by impairments in social communication and social interaction and the presence of restricted and repetitive behaviors, interests, or activities –5. At thBuilding an automated diagnostic and predictive model of ASD is timely as many studies have adopted machine learning approaches to identify significant biomarkers that include both behavioral and biological features. For instance, Duda and colleagues 2016) applied machine learning to distinguish ASD from attention deficit hyperactivity disorder (ADHD) using the Social Responsiveness Scale for children between 5 to 13 years old . Bone et16 applieMachine learning (ML) has been introduced to the neuroimaging field to identify the atypical brain regions in individuals with ASD. The support vector machine (SVM) is an algorithm that avoids overfitting and is known for high classification accuracy without requiring large sample sizes. It has been used to classify ASD from NT participants using extracted features from functional connectivity metrics and grey matter volume –37. OtheSpecifically, classification across a heterogeneous population as is true in autism is challenging –45, partASD drivers or markers are likely the result of a complex interaction of factors with no single factor  driving the system. As such, traditional statistical tools  that search for univariate drivers of ASD are unlikely to find consistent patterns. Thus, ML techniques that explore large search spaces for multivariate interactions are both needed and becoming popular in helping to elucidate the complex interactions in systems such as ASD. Our study employs one such ML tool: an evolutionary algorithm  called tThis study examines the utility of using the CCEA for feature selection in ASD, particularly to address traditional statistical challenges associated with datasets having small sample sizes and a large number of feature measurements. Additionally, the selected features are validated and used for diagnostic classification by applying a separate and more traditional ML classifier  algorithm). The dataset in the present study has a relatively large number of features, consisting of both behavioral and neuroimaging measurements. Although some behavioral features may seem less objective compared to an assessment of neuroanatomical features, they are often easier to obtain and more pragmatic for children with ASD especially if they have strong psychometric properties. Clinicians seldom place a child with ASD in a scanner to obtain neuroanatomical information before conducting behavioral assessment for the purpose of diagnosis and treatment. The combination of behavioral measures with neuroanatomical information supports the value of making brain-behavior connections that will advance our understanding of ASD. In the present study, the behavioral measurements include scores of language ability, intellectual ability, and ToM. The neuroimaging measurements include brain volume, brain surface area, cortical thickness, and cortical curvature extracted from MRI whole-brain T1-weighted scans. These features were collected from a total of 28 children ages 7 to 14, of which 9 children had been diagnosed with ASD . Only a Using the CCEA, we aim to identify discriminative biomarkers and behavioral features to help develop an automated diagnostic and predictive system for ASD. We believe this is a pioneering study for:Selecting discriminative biomarkers among children from 7 to 14 years old and classifying ASD.Including both behavioral and neuroimaging measurements in the feature selection model for better prediction and understanding of ASD.Identifying models (sets of features) that most strongly correlate to children with ASD given a dataset with a relatively small sample size  and a large number of features .Developing a predictive ML model using input features selected by the CCEA.A total of 9 children with ASD  and 19 NT children , ages 7–14, were enrolled in the study. In addition to the behavioral assessments described below, the ASD group also completed the ADOS-2 and the Social Communication Questionnaire-Lifetime version (SCQ)  to confiThe present study was approved by the University of Vermont (UVM) Research Protection Office and IRB committee. The specific staff member who approved the study protocol, 19–0005, was Karen Crain. Consent forms were obtained from each participant’s legal guardian and assent forms were obtained from each participant.All children participated in 2–3 hours of baseline behavioral assessments that included the Comprehensive Assessment of Spoken Language (CASL) , the UniThe CASL is an orally administered research-based assessment consisting of 15 subtests measuring language for individuals ranging from 3 to 21 years of age. For the present study, only those basic subsets that establish the CASL language core are used: Antonyms, Sentence Completion, Syntax Construction, Paragraph Comprehension, and Pragmatic Judgment. Reliability for the core subtests ranges from 0.80 to 0.90 with composite scores ranging from 0.92 to 0.96. Test-retest reliability for individual subtests range from 0.65 to 0.95 with core composite coefficients from 0.92 to 0.93. The UNIT-2 is a multidimensional assessment of intelligence for individuals with speech, language, or hearing impairments. It consists of nonverbal tasks that test symbolic memory, non-symbolic quantity, analogic reasoning, spatial memory, numerical series, and cube design. Reliability studies include internal consistency, test-retest, and scorer reliability. Subtest coefficients range from 0.88 to 0.96 and 0.93 to 0.98 for the composites. Content, criterion and construct validity were largely correlated with other cognitive measures and demonstrated the value of the UNIT-2 for the assessment of diverse groups of children.α = 0.91 at T1 and α = 0.94 at T2) and strong construct validity  [ToM is a core deficit in ASD that is often used to explain the social impairments of the disorder. ToM is the ability to reason about the thoughts and feelings of self and others, including the ability to predict what others will do or how they will feel in a given situation on the basis of their inferred beliefs , 58. Chip < .01) , 56, 59.t-tests found that NT participants scored significantly higher (p<0.05) than ASD participants on the CASL, UNIT-2 full scale, ToMTB, ToMI-2 total, ToMI-2 early subscale, ToMI-2 basic subscale, ToMI-2 advanced subscale, as well as ToMI-2 single items of surprise, embarrassment and desire-based emotion. In its psychometric development, the ToMI and ToMTB were tested against children with and without autism with scores on both measures, discriminating those children with a diagnosis of ASD from those without such a diagnosis. These measures of ToM also showed a delay in development of early developing and basic ToM skills and a failure to achieve more advanced ToM skills [In selecting potential features for the CCEA, we included 13 behavioral features. These included the total score of the CASL, full scale score of the UNIT-2, abbreviated score of the UNIT-2, total score of the ToMTB, total composite mean of the ToMI-2 , early subscale mean of the ToMI-2 , basic subscale mean of the ToMI-2 , advanced subscale mean of the ToMI-2 . Drawn from a larger study examining the neural mechanisms underlying ToM and emotion recognition , we alsoM skills –56.mm2 field of view and 225 slices. Participants watched three videos at home before coming to the MRI center. The first was a cartoon video explaining what an MRI is, and what one might experience while lying in an MRI scanner [All data were acquired using the MRI Center for Biomedical Imaging 3T Philips Achieva dStream scanner and 32-channel head coil at the University of Vermont (UVM). Parameters for T1 acquisition were TR 6.4s, TE 2.9s, flip angle 8 degree, 1mm isotropic imaging resolution with a 256 x 240  scanner . The sec scanner . Brain a scanner , which r scanner . There wFi represents a risk factor i whose value lies in the range ai; and the symbol ∧ represents a conjunction . One benefit of the CCEA is that it produces parsimonious models that are correlated with a select category . The models generated by the evolutionary algorithm can be described by their order or total number of features in the conjunctive clause. One example of a parsimonious second order conjunctive clause is: a person with a right hemisphere isthmus cingulate volume of 3,300–4,100 mm3 AND a right hemisphere posterior cingulate volume of 4,100–6,200 mm3 is more likely to have ASD than someone who does not meet these criteria. The CCEA was able to generate models that ranged from 1st to 5th-order in the present study. Since we are only interested in the most parsimonious  to draw meaningful conclusions and to avoid the overfitting that often occurs with higher-order models, we focused on second-order models  with the highest fitness (PMF) in which all of the features identified were brain anatomical features. Because of our desire to explore the predictive capability of the behavioral features, we also expanded our analysis to include third-order models  in which behavioral features were included.We used an evolutionary algorithm to identify the features associated with ASD. The CCEA is a machine learning tool that searches for both the combinations of features associated with a given category  as well as their corresponding range of feature values . The CCEp-value and thus, is not constrained by issues associated with what threshold is “significant” [The fitness of each conjunctive clause (CC) is evaluated using the hypergeometric probability mass function (PMF), and only the most-fit conjunctive clauses are saved. The hypergeometric PMF is not a ificant” –70. To pIn order to further validate the CCEA’s selection of features capable of discriminating between children with and without ASD, we built a separate KNN classification model and used leave-one-out cross validation on all 28 subjects. The KNN is a classification algorithm that assumes that things that exist in close proximity  are more similar. In this study, each subject was classified into one of two output classes  based on a plurality vote of its neighbors, with the subject being assigned to the class most similar to its k-nearest neighbors. When k = 1, then the subject is assigned to the class of a single nearest neighbor . Leave-oWhen using the CCEA for feature selection, 2438 CCs  were generated ranging from first-order to fifth-order. The PPV of the 2438 models ranged from 46.47% to 100% and their class coverage ranged from 42.86% to 100%. Among these models, we looked for the most parsimonious  to draw meaningful conclusions and to avoid the overfitting that often occurs with higher-order models. As a result, we selected 8 second-order models  with the highest fitness (PMF) among the total 520 second-order models. These 8 “best performing” models each have 100% PPV and 100% class coverage, see mm3 AND left rostral middle frontal gyrus volume was within the range of 20,000 to 25,000 mm3 would be classified as having ASD. The volume of the left hemisphere posterior cingulate gyrus and the volume of the right hemisphere isthmus of the cingulate gyrus were the two features to appear most frequently  across all second-order models, suggesting that the volume of cingulate gyrus is a potentially important biomarker for ASD. Using CC 113  as an exBecause of our desire to explore the predictive capability of the behavioral features, we expanded our analysis to include third-order models . There were 651 third-order models in total; while some consisted only of 310 anatomical brain features, others included two behavioral features plus one brain anatomical feature. We selected the 6 best-performing third-order models with the highest fitness (PMF); each had 100% PPV and 100% class coverage, see Using CC 46  as an exAs mentioned earlier, a cohort of new subjects comprising 2 ASD and 5 NT children were enrolled at a later time. This later cohort was combined with the 21 subjects used in CCEA to cross-validate the KNN classifiers. Using the 8 unique features of the second-order model, the KNN (k = 3) achieved 89.29% classification accuracy (ACC), where 17 of the 19 NT subjects and 8 of the 9 ASD were classified accurately; see Using the 9 unique features of the third-order model, the KNN (k = 7) validation accuracy fell to 78.57% compared to when using the 8 unique features from the second-order model, where 15 of the 19 NT and 7 of the 9 ASD subjects were classified accurately. See Given the better ASD classification performance of the second-order neuroanatomical features, and that the behavioral measurements/features are relatively easier to collect among children with ASD, it was important to explore whether the ASD prediction results might be improved when the behavioral features were combined with the second-order features. As a result, we added the three behavioral features  from the third-order models to the 8 second-order brain anatomical features and cross-validated a new KNN (k = 2) classifier. With the total of 11 unique features, a validation accuracy of 85.71% was achieved with 16 of the 19 NT subjects and 8 of the 9 ASD being classified accurately. See the confusion matrix of To further examine whether the KNN classifier could discriminate subjects with ASD and NT, we developed three classification models–one using the 8 unique features from the second-order models, one using the 9 features from the third-order models, and one using the 11-feature model . The best classification accuracy was achieved using a balanced training set that consisted of 6 NT and 6 ASD subjects, among which only 2 of the 6 ASD subjects were not part of the original CCEA feature selection. The remaining 16 subjects were used for testing (13 NT and 3 ASD).The KNN (k = 1) results for the second-order model features are shown in The KNN (k = 3) classification accuracy for the third-order model features was 81.25%, with all 3 of the ASD subjects and 10 of the 13 NT subjects correctly classified. Of the 3 misclassified NT subjects, 2 were included in the original CCEA feature selection. See Lastly, the KNN (k = 3) predictions for the combined 11-feature model  are shown in This study used a new ML feature selection tool, the CCEA, to identify biomarkers and behavioral features capable of successfully discriminating between children (7 to 14 year of age) with and without ASD given a small dataset collected from a single research site. ML tools have long been applied to ASD research, but it remains a far-reaching goal to build a diagnostic system for ASD that incorporates both feature selection and prediction. Previous studies face the challenge of using datasets across different research sites for classification purposes, rather than identifying input variables most closely associated with ASD  , 73, 74.The present study demonstrated exceptionally good performance  of the features identified by the CCEA. The selected CCEA features from the parsimonious second and third order models included volume, area, cortical thickness, and mean curvature in specific regions around the cingulate cortex, frontal cortex, and temporal-parietal junction areas as biomarkers for ASD . Such findings are consistent with previous literature suggesting that individuals with ASD have abnormalities in these brain regions , 75–77, It is impressive that the KNN classifiers are able to achieve such high classification accuracy given our sample size, and validation of the discriminant features selected by the CCEA models. In particular, the KNN classifiers perform better using the second-order neuroanatomical features than the third-order feature models, which emphasizes the importance of focusing on parsimonious models selected by the CCEA. In addition, the KNN achieved the highest classification accuracy when adding the behavioral features from the third-order models to the neuroanatomical features from the second-order models. In most cases, neuroimaging measurements are conducted along with behavioral assessments. As the third-order models are included to explore the potential role that behavioral features might play along the side of neuroanatomical features, it is convincing to see the highest classification accuracy achieved when combining the behavioral features and the neuroanatomical features. These findings highlight the heterogeneous and multi-facet characteristics of ASD itself. Thus, although it is more difficult to implement MRI among children with ASD, such findings support the idea that neuroanatomical measurements increase confidence in diagnosis. It also suggests that a good ASD prediction model should consider including both behavioral and neuroanatomical features to establish brain-behavior connections to advance our understanding of ASD.This study further demonstrates the robustness of the CCEA as a feature selection methodology. The accuracy of these features when used as input variables in the KNN classifier suggests their potential to help clinicians and researchers target specific domains in ToM in treating the social challenges most often seen in children with ASD. The implications of our findings for clinical researchers reinforce earlier findings regarding the brain-behavior connections for children with and without ASD related to ToM understanding , 78–80. The present study has established important biomarker candidates of ASD. These biomarker candidates support previous research adopting traditional neuroimaging measurements identifying similar brain regions to explain the abnormalities in ASD , 23–31. S1 Data(CSV)Click here for additional data file.S2 Data(CSV)Click here for additional data file.


Acoustic rhinometry (AR) has been used as a specific test for nasal patency.this study aimed to set the reference values for nasal cavity cross-section geometry in healthy adults through AR.this is a clinical prospective study.thirty volunteers  without signs of nasal obstruction and aged between 18 and 30 years were enrolled in this study. They were assessed before and after being treated topically with a nasal vasoconstrictor drug. Their nasal cross-sectional areas were measured at the three dips of the rhinogram, corresponding respectively to the nasal valve (CSA1), the anterior (CSA2), and the posterior (CSA3) region of the inferior and middle turbinate.the mean areas (±SD) for 60 nasal cavities before nasal vasoconstriction were: 0.54±0.13cm2 (CSA1), 0.98±0.31 cm2 (CSA2), and 1.42±0.44cm2 (CSA3). After vasoconstriction, the mean values of the three segments analyzed were significantly larger (p<0.05). Gender was not a statistically significant variable.The nasal cross-sectional areas obtained for adults may be used for control purposes when studying patients with nasal obstruction, in conjunction with the nasal volume values previously reported by our group. Instrumental methods were developed in the past few decades to objectively check nasal patency and allow for further clinical finding confirmation. The most frequently used technique is rhinomanometry, in which patency is estimated based on nasal airflow resistance, from the simultaneous verification of flow and transnasal pressures generated during breathing at rest. Despite its validity, nasal airflow resistance has its limitations, the most important being it is flow-dependentNasal cross-sectional area also began to be measured by acoustic rhinometry from the study conducted by Hilberg et al.Today acoustic rhinometry is broadly recognized as a specific test for nasal patency. Normal cross-sectional area values have been reported by many authorsTherefore, in the study conducted in our laboratory, we first defined the volumes of three areas in the nasal cavity  of adults without evidences of nasal obstructionSixty nasal cavities were analyzed from a sample of 30 adult volunteers without evidences of nasal obstruction , after they signed a free informed consent form. Participants were selected based on answers given in a questionnaire designed to identify past and present signs and symptoms of nasal obstruction and on nasal airflow verification using Glatzel’s mirror, as described in the first paper of the seriesThis study was approved by the Research Ethics Committee of our institution under permit 070/2002-UEP-CEP.Tests were conducted using an Eccovision Acoustic Rhinometer (HOOD Laboratories), which consists of a sound source (loudspeaker) distally positioned in relation to a 24 cm tube equipped with a microphone for acquisition in its proximal portion. The rhinometry tube is placed against one of the nostrils; a sound wave generated by the loudspeaker propagates through the tube, passes through the microphone and enters the nasal cavity. Variations in the cross-sectional area, i.e., constrictions reducing cavity lumen, reflect the sound waves back into the rhinometry tube. Pressure signals sensitize the microphone and are amplified and digitized. A computer equipped with specific software is used to analyze the signals. The system is shown on 2) and distances on the axis X (in cm) as seen in Nasal cross-sectional areas are calculated from the echo intensity values. Distance to site of constriction is calculated based on wave speed and time of echo feedback. The data is converted into an area-distance function and presented on the computer monitor in the shape of a graph - the rhinogram - in which areas are shown in a semi-logarithmic scale on axis Y  on each nostril after nasal hygiene. The values used in our analysis are the averages for the three measurements conducted in three technically acceptable curves for each condition.As discussed in detail in the first paper of the series2) obtained in the second dip of the area-distance curve which corresponds to the nasal valve region (CSA1), in the third dip which corresponds to the anterior portion of the middle and inferior turbinate (CSA2), and in the fourth dip which corresponds to the posterior portion of the middle and inferior turbinate (CSA3), as seen in For analysis purposes, we considered the nasal cross-sectional areas . Statistical significance difference for related paired samples  was assessed through Student’s T test. Significance was assigned when p<0,05.Statistical analysis showed no significant differences between the CSA averages verified in the right (R) and left (L) sides before and after vasoconstrictor administration. Therefore, both sides were considered as independent cavities and new measurements were taken and averages calculated for all 60 nasal cavities (right and left), as also seen in The recommendations for technical specifications and standard operating procedures from the European Rhinological Society2 indicated as normal by Hilberg and Pedersen7, we verified that only one of the 60 cavities analyzed had an area smaller than the value defining adequate nasal patency, strongly indicating that our sample was indeed made up by subjects without nasal obstruction. It is important to mention that in this case specifically the subject’s contralateral cavity had an area of 0.78cm2, thus compensating for the likely structural or functional unilateral obstruction and sensation of adequate nasal patency. Secondly, the tests were carefully performed to take into account and control the variables pointed by other authorsTwo methodological issues deserve special attention. Firstly, the group of 30 individuals analyzed in this study were conveniently selected from a population of 54 apparently health individuals based on subjective reports of normal nasal patency sensation and no history of nasal functional and anatomical disorders, as also seen in control groups enrolled by other authorsLet us now analyze the nasal cross-sectional areas acquired in this study and their differences in comparison to those reported in the literature for normal adults, as seen in 2 and 0.59cm2, thus validating the results published in this paper. The papers cited above are quite recent, and two used the same equipment employed in our study. In all others2 and 0.78cm2. This difference may be attributed to the fact that most studies - specially the ones done longer ago - used olive-type nasal adapters which ae known to introduce nasal cavity deformation, thus leading to overestimated cross-sectional area measurementsFor simplification purposes, we will now compare our CSA1 values (before vasoconstriction) - the standard measurement in all studies published on this subject - to the ones reported in other papers. The difference between average CSA1 values was never beyond 10%, when comparing our data set to that of various authorsIn some of the rhinograms it was not possible to identify the dips related to CSA2 and CSA3, more specifically after vasoconstriction. One may speculate that this resulted from absence of constrictions along the nasal cavity after the valve that could modify sound reflection and thus create a dip in the rhinogram, similarly to what would occur in a straight tube. We cannot, however, discard the possibility that this was a technical artifact.In terms of gender differences, differently from what was observed in the larger volumes seen in males in the areas close to the valve and turbinatesFinally, we should comment on the results obtained under vasoconstriction. This procedure is performed to identify structural changes in the nasal fossae, considering that the functional effect of the mucosa is removed. Similarly to what was seen for nasal volumesThis study used acoustic rhinometry to determine the reference values for nasal cross-sectional areas to be used, for comparison purposes, in the analysis of adults with functional and/or anatomical nasal obstruction. The findings we gathered reinforce the relevance of rhinometry as a valuable tool to enhance the assessment of nasal patency and better understand nasal and respiratory physiology.


An above-water camera was used to identify and measure the different phases of the underwater pullout techniques, which was found to be a highly accurate methodological approach (ICC = 0.97). From the 150 trials analyzed, three different pullout techniques were identified: the Fly-Kick First technique, the Combined technique and the Pull-Down First technique. Although the most common underwater pullout technique utilized by elite competitive breaststroke swimmers was the Combined technique (n = 71), followed by the Fly-Kick First technique (n = 65) and the Pull-Down First technique (n = 14), it was observed that technical selection deviates according to gender. This indicates that male and female swimmers should not be coached adhering to the same technical model. This study found no significant difference in terms of performance outcome with respect to each of these techniques, indicating that technique selection should be guided by one's individual preference. It was concluded that the results of this study will serve as an up-to-date resource for coaches and swimmers working with elite breaststroke swimmers and as a useful insight to current underwater pullout trends.Since the rule change permitting the inclusion of one dolphin kick during the underwater breaststroke pullout phase following a swim start or turn, there has been an emergence of several different pullout techniques adopted by elite swimmers. The aim of this study was to characterize the underwater breaststroke pullout technique trends and to assess the effectiveness of each technique as utilized by elite male and female swimmers. The sample included 60 swimmers ( In competitive swimming, rules and regulations associated with performing starts and turns have evolved over the decades and are governed by Fédération Internationale de Natation (FINA). A significant amendment in 2005 to the breaststroke event, permitted the inclusion of one butterfly kick during the underwater breaststroke pullout following a start or turn. A pullout is defined as the period from toe immersion following the start, or toe-off at the turn wall, until the swimmer breaks the water surface to commence free breaststroke swimming. After subsequent modifications, the current FINA  SW 7.1. The underwater phase has been identified as the most important determinant of start and turn performance, as this is when the swimmer is traveling fastest through the water  ascertain the breaststroke pullout technique trends, as determined by the location of the fly-kick placement, across a range of international competitions, and (2) to assess the effectiveness of these pullout techniques as utilized by elite male and female swimmers across all competitive breaststroke events. It is hypothesized that a range of pullout techniques will be observed across swimmers and based on the findings of previous experimental studies  across the 50 m , 100 m  and 200 m  breaststroke events. This totalled 150 race entries across the 50 m , 100 m  and 200 m  that were analyzed for this study. All the swimmers specialized in breaststroke, and were classified as elite based on the FINA points ‘Level 1' qualifying standards (≥875 pts) set by Ruiz-Navarro et al. , was used for all competition analysis. The system comprised of a single side-on panning Panasonic HC 1500 camera , mounted at the highest point possible within the respective venues, usually around the 25 m mark of a 50 m pool to record all swim races. This experimental set-up is typical within a high-performance race analysis competition setting, demonstrating the study's ecologically valid approach  reached 15 m  1st glide (A.1)—from toe immersion (dive start) / toes having left the wall (turn exit) to first movement deviating from a streamline position (common instances of deviation: hand separation/start of dolphin kick preparation phase/first upper body movement).2) Dolphin kick (L.1 to L.5).3) Pull down (A.1 to A.3)—time between beginning of hand separation (breaking the streamline position) to the end of pull down—end of backward movement of hands finishing at the hips (or thighs).4) 2nd glide (A.3)—time from end of pull down to the start of arm recovery—as the elbow starts flexing and the hands start forward movement.5) Arm recovery (A.3 to A.4)—time between start of arm recovery to the instance when the arms are fully extended.6) Leg recovery (L.6 to L.7)—time between first initiation of knee flexion, to position just prior to first backward movement of feet.7) Propulsive kick (L.8 to L.9)—time between first backward movement of feet, to end of kick—as the feet come together ending inward lateral movement.8) 3rd glide (A.5 and L.9)—time between feet coming together and first lateral movement of hands or as the head breaks water surface.Once the phases were defined, the underwater pullout technique could be determined based on the temporal order in which the phases were executed. For example, Through observation of all 150 trials captured across the four competitions within this study, two further technical trends were identified and categorized, relative to the Fly-Kick First technique In defining the three techniques across the participants in this study; a visual representation provided below highlights the primary temporal variation in arm and leg action sequencing during the pullout progression across all techniques. The below representations exclude all actions occurring after the arm action “A.2” (i.e. prior to the 2nd glide), in order to place a greater emphasis on the underwater temporal phase variation between the three techniques.post-hoc correction was used to assess the differences between underwater pullout techniques. The significance level across all statistical tests was set at p < 0.05. The eta square (η2) was used to assess the magnitude of the effect size, with: (i) without effect if 0 < η2 ≤ 0.04; (ii) minimum if 0.04 < η2 ≤ 0.25; (iii) moderate if 0.25 < η2 ≤ 0.64; and (iv) strong if η2 > 0.64 , 100m  or 200m  breaststroke race events.A summary of the descriptive statistics calculated for each technique across all race distances is displayed within Post hoc results indicated a difference between the Combined technique and Pull-Down First technique (p = 0.05).Observationally, it was noted that nine swimmers changed their underwater pullout technique during the 200 m race event, and three swimmers modified their pullout technique in the 100 m for the start vs. turn. The implications of this observation will be further explored within the discussion.The purpose of this study was to characterize the breaststroke underwater pullout technique trends utilized by elite swimmers within a competition setting across all race events and to assess the effectiveness of each. The context underpinning this study was to consider the inclusion of the fly-kick during the underwater pullout phase as a consequence of FINA,  SW 7.1 rUsing footage from key international competitions, three different pullout techniques were identified in this study: the Fly-Kick First technique (fly-kick is initiated and completed prior to pull-down), the Combined technique  and the Pull-Down First technique (pull-down is completed prior to fly-kick). Therefore, our first hypothesis that a range of pullout techniques will be observed across swimmers was accepted. These three techniques differ slightly with respect to Seifert et al.  who idenThe results of this study show that across all race distances, the most common underwater pullout technique utilized by elite competitive breaststroke swimmers  following a start and turn was the Combined technique, followed by the Fly-Kick First technique and the Pull-Down First technique respectively. This differs from Seifert et al.  who founInterestingly, when the dataset was filtered by gender, it was observed that male and female elite breaststroke swimmers tended to favor different techniques during the underwater pullout phase. Male swimmers were observed to utilize the Combined technique most often followed by the Fly-Kick First technique across all race distances, whereas female swimmers favored the Fly-Kick First technique followed by the Combined technique. It has previously been established that body morphology directly affects a swimmer's hydrodynamic resistance, with the majority of literature suggesting that males experience increased drag compared to females due to differences in body shape . Regardless, this is the first paper to qualitatively report the underwater breaststroke pullout technical trends across multiple competitions and thus provides a novel contribution to the research area and swimming community. Future studies should explore the underwater pullout trends further in terms of spatiotemporal characteristics and examine the potential discriminant factors associated with performing these different techniques. For example, it would be useful to quantify the break-out distances associated with each technique to assess if the time to 15 m was influenced by surface swimming, rather than the differing pull-out techniques. It is also recommended that the velocity profiles of each pullout technique be investigated. As the breaststroke pullout follows the dive and wall push-off phases within a race lap; the primary aim of the pullout should be to maintain the speed generated following these phases. This is achieved by reducing drag in optimizing body form and minimizing velocity degradation through beneficial use of leg and arm actions prior to the free-swimming phase. Velocity profiles could allow for a greater understanding of each of the techniques in relation to these instances and facilitate coaches to make more informed decisions on technique selection for optimizing start and turn performance.Considering the methodological limitations of this study, the results should be interpreted cautiously until future investigations confirm our technique observations The findings from this study will yield multiple practical implications for coaches and sport scientists. Given the significant contribution of the start and turn in breaststroke performance at the elite level, large importance should be placed on the pullout phase during training practices. This study demonstrates that technique selection is largely individual, but coaches should conduct appropriate biomechanical testing to ensure that the fastest technique is being used for each individual's start and turn performance. The technique used should also consider the physiological cost, as the selection of the Combined technique and Pull-Down First technique may influence the breakout distance (as reported by McCabe et al., This was the first study of its kind to provide an in-depth analysis focusing solely on the breaststroke pullout using a large cohort of elite swimmers during competition. This study identified three common breaststroke pullout techniques used by elite swimmers during multiple key international competitions. Based on qualitative observations, the most frequent pullout technique was the Combined technique, with indications this was changing toward a Fly-Kick First technique preference in recent years. Male and female swimmers appear to utilize different underwater pullout techniques; therefore, it is recommended they should not be coached adhering to the same technical model. Although there was no difference in performance across techniques, it is important that swimmers are proficient in their chosen technique. The results from this study will serve as a resource for coaches and swimmers working with breaststroke swimmers competing at the highest international level.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Manchester Metropolitan University Ethics Committee. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.CM and ET conceived and designed the study. EM performed all data analysis. RH produced the Figures. CM, ET, and EM carried out the drafting of the manuscript. All authors reviewed the manuscript and approved the submitted version.Author ET was employed by Forethought Pty Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Simultaneously with the development of the COVID-19 vaccination plan for minors, it is critical to understand the reasons related to parental COVID-19 vaccination hesitancy. This study aims to determine the reasons associated with vaccination hesitancy among parents, and the prevalence and the characteristics of the parents who are hesitant to allow their children aged between 5 to 11 years old to be administered the COVID-19 vaccines. A web-based questionnaire was used to perform this study between May 2022 to September 2022 in Saudi Arabia (SA). Several factors, personal and social, affected the participants’ willingness to vaccinate their children with the COVID-19 vaccines. The age of the parents was found to have a significant impact on their decision to vaccinate their children. Those between the age of 40–49 years of age were the most willing to vaccinate  compared to those 50 years or older who were most resistant to vaccination. Female participants were more resistant to vaccinating their children compared to their male counterparts. Saudis were more resistant to vaccinating their children compared to the non-Saudi participants. Those private sector-employed parents were the most willing to vaccinate (16.6%), followed by those working in the governmental sector (13.8%). About 40.7% of non-healthcare workers were resistant to vaccinating their minor compared to healthcare workers (8.7%). In conclusion, the study presents several factors that affect the parental willingness to vaccinate their children in SA. These factors should be properly addressed when developing public health strategies to promote the COVID-19 vaccination of children in SA. The pandemic coronavirus disease COVID-19) has resulted in increasing morbidity and mortality rates, as well as increasing the burden on hospitals and healthcare systems around the world 9 has res. TogetheOn 29 October 2021, the Food and Drug Administration (FDA) allowed children aged between 5 to 11 years old to administer the Pfizer-BioNTech vaccine in two doses (10 µg and 0.2 mL for each dose) 21 days apart. This dose is less than the 30 µg administered by adults and older children ,13. It iOn 21 December 2021, KSA started to vaccinate children aged between 5–11 years to protect them from getting the infection and reduce their morbidity and mortality resulting from COVID-19. This vaccination campaign also assisted them in resuming normal lives and attending school. In order to increase the rate of vaccination among children aged between 5 to 11 years old in KSA, vaccination hesitancy among their parents should be considered and assessed. Vaccination hesitancy is defined as unwillingness or reluctance to administer the vaccine or have one’s minors receive vaccine against infectious disease, although the vaccination has been approved to be effective and safe .A vaccine that is both safe and effective is not enough to halt this pandemic; a high level of general public acceptance of vaccines is also required. Many studies have been conducted to evaluate the public perception of the COVID-19 vaccination and vaccination hesitancy . GloballSimultaneously with developing the COVID-19 vaccination plan for minors, it is significant to understand the reasons related to parental vaccination hesitancy and COVID-19 vaccination. Thus, the current study aims to determine the reasons associated with vaccination hesitancy among parents, and the prevalence and characteristics of the parents who are hesitant to allow their children aged between 5 and 11 years old to administer the COVID-19 vaccines.This is cross-sectional study was carried out in KSA between May 2022 to September 2022, using a snowball sampling technique. A web-based questionnaire was created using Google Forms and distributed to study participants, i.e., parents, to define the proportion who reported vaccination hesitancy, their reasons behind it, in addition to determining their beliefs toward COVID-19 immunization. The questionnaire consisted of 15 questions and took approximately 7 min to answer all the questions. It included questions related to participants’ characteristics, participants’ experience and attitudes towards vaccination, factors affecting parents’ willingness to vaccinate their children, and reasons behind parental willingness or parental refusal to vaccinate their children. The online questionnaire was disseminated through social media Apps, for instance, Instagram, Twitter, Facebook, and WhatsApp. The invitation letter explained the objectives of our study as well as the estimated time needed to finish the survey. Responders will be kindly requested to the send questionnaire to their relatives and friends. Only 344 participants out of 400 responded completely to our questionnaire.The eligibility criteria are parents who have minors aged between 5 to 11 years, and those who are residents of KSA during the time of questionnaire submission. We did not provide an age limit for responders. We targeted minors below 12 as this group is significantly affected by parental decisions in contrast to more mature children; furthermore, KSA recently they have included this age group in the COVID-19 campaign. If participants had more than one minor between the target age limit, they were requested to limit their responses to only one minor who was selected at random via the system.We calculated the sample size using the Raosoft sample size calculator program and according to the total number of KSA population 35,761,605, based on a 95% confidence interval (CI); hence, the sample size was estimated to be 385 as the minimum.p-value of <0.05 denoted statistical significance. The frequencies and descriptive statistics were used to express categorical data on the sociodemographic factors of the parents. To detect parental characteristics linked with their acceptance to immunize minors aged 5 to 11 years with COVID-19 vaccination, we used chi-squared analysis for categorical data.The data were analyzed by using GraphPad Prism 5 software . A The study was conducted in accordance with the Declaration of Helsinki and approved by Taibah University College of Medicine Research Ethics Committee (IORG0008716—IRB00010413), May 2022. The online informed consent was taken from all responders who meet the eligibility criteria after completing screening questions. Responders clicked on the checkbox on the online screen to indicate their informed consent of participation.Overall, 344 subjects participated in this study, of which 40.7% were male (n = 140), and 59.3% were female (n = 204). Most of the study participants were between 30 and 49 years old (67.4%), two-thirds were Saudi (n = 228), and almost half were educated to a bachelor degree level. The study participants were from across Saudi Arabia, with the highest percentage of 35.17% of the participants from Madinah (n = 121). Two-thirds of the study participants were employed either in the private sector (n = 114) or in the public sector (n = 124), with almost one-third of those employed being healthcare workers. The detailed characteristics of the study participants are described in The study participants were surveyed to examine their trusted information source and their previous vaccination experience. The majority of the participants (over 82%) indicated trust in information provided to them by healthcare (n = 284). On the other hand, information found in newspapers or magazines was least trusted (n = 50) by the study participants. One-third of the study participants (n = 115) previously received the seasonal influenza vaccine, while over three-quarters of them (n = 271) received the COVID-19 vaccination. Further information is found in Upon asking whether participants were willing to vaccinate their children with the COVID-19 vaccines or not, over one-third of them were willing to vaccinate their children (n = 130). Interestingly, almost half of the participants (n = 170) were resistant to the vaccination, and about 12.8% of them (n = 44) were unsure.Several factors, personal and social, affected the participants’ willingness to vaccinate their children with the COVID-19 vaccines. The age of the parents was found to have a significant impact on their decision to vaccinate their children. Those between the age of 40–49 years of age were the most willing to vaccinate  compared to those 50 years or older who were most resistant to vaccination.Other factors affecting the vaccination acceptance were participants’ gender, nationality, occupation, and being a healthcare worker. Female participants were more resistant to vaccinating their children (60%) compared to their male counterparts (40%). Saudis were more resistant to vaccinating their children (64.1%) compared to the non-Saudi participants (35.9%). Those private sector-employed parents were the most willing to vaccinate (43.8%), followed by those working in the governmental sector (35.3%). About 52.4% of non-healthcare workers were resistant to vaccinating their minors in comparison to healthcare workers (38.9%). A detailed description of the factors affecting participants’ willingness to vaccinate is described in Previous vaccination of the parents with either the seasonal flu (influenza) or COVID-19 vaccines positively affected their willingness to vaccinate. About 17.7% and 36% of those who were vaccinated with influenza and COVID-19 vaccines, respectively, were willing to vaccinate their children with the COVID-19 vaccines. On the other hand, 39.3% and 18.9% of those who were not vaccinated with the flu and the COVID-19 vaccines, respectively, were not willing to vaccinate their children with the COVID-19 vaccines.The participants’ trusted source of information did not affect their willingness to vaccinate their children with the COVID-19 vaccines. Interestingly, almost half of those who trust healthcare workers as their sole source of information was not willing to vaccinate their children. The description of the participants’ attitude towards vaccination based on their previous vaccination and their trusted source of information is described in The latest pandemic of COVID-19 took the world by storm, affecting every aspect of life globally. Saudi Arabia was not different from the rest of the world in being affected by it, despite the strict and progressive measures it took to limit the pandemic’s impact on the country . DespiteThe percentage of participants who have received the COVID-19 vaccine are similar to the officially reported vaccination coverage in Saudi Arabia . SeveralInterestingly, the level of parents’ education did not impact their willingness to vaccinate their children in our study. This finding was in line with what Sayed and Soares et al. described ,50. On tExpectedly, this study demonstrated that parents’ experience with vaccination—specifically, vaccination uptake—significantly affected their decision to vaccinate their children. Those who received either the flu (influenza) or COVID-19 vaccines were more likely to vaccinate their children. Almaghslah also described similar results in Saudi Arabia where participants varied in their vaccination status , which wAlthough every attempt was made to conduct and present the study in the best possible way, this study is not without limitations. The cross-sectional nature of the study limits the interpretation of the presented results to the sampled population at the time of the study’s conduction. Such a limitation may limit its comparability with other studies, which may have been conducted at different times in different circumstances. Additionally, the relatively small sample size of this study and the very small number of participants from certain regions could limit the generalizability of the study’s findings. A total of 344 participants from across Saudi Arabia participated in this study, whereas over 1400 participants participated in a similar study from a single region in Saudi Arabia .The study presents several factors that affect the parental willingness to vaccinate their children in Saudi Arabia. These factors should be properly addressed when developing public health strategies to promote the COVID-19 vaccination of children in Saudi Arabia. Future studies should include a more participants from Saudi’s different regions to have a more representative results that could better direct public health policies in Saudi Arabia.


The operational costs of wastewater treatment plants (WWTPs) are mainly driven by electric power consumption, making the energy-efficient operation an all-time present target for engineers and operators. A well known approach to reduce the demand for purchased electricity is the operation of an anaerobic sludge stabilisation process. Although anaerobic digesters make it possible to recover large quantities of energy-rich methane gas, additional strategies are required to handle the increased internal return flow of nitrogen, which arises with the sludge dewatering effluent (SDE). SDE treatment increases the oxygen demand and in turn the energy required for aeration. In this study, different SDE treatment processes were compared with regard to the treatment in mainstream, sidestream nitritation, as well as nitritation combined with anammox for two-stage and single-stage WWTPs. Although SDE treatment in sidestream nitritation was found to have no effect on the energy demand of single-stage WWTPs, this concept allows the treatment capacity in the activated sludge tank to be raised, while contributing to a high nitrogen removal under carbon limitation. In contrast, SDE sidestream treatment showed great potential for saving energy at two-stage WWTPs, whereby sidestream nitritation and the further treatment in the first stage was found to be the most efficient concept, with a savings of approx. 11% of the aeration energy. Wastewater treatment plant (WWTP) concepts usually encompass a mechanical treatment by screens, grit removal, and primary sedimentation to remove particular and organic matter. The subsequent biological treatment serves to remove soluble substances by microbial conversion. Especially the biological treatment is highly energy-intensive, since aeration is required to cover the oxygen demand of the bacteria involved. The energy consumption of WWTPs has been intensively analysed in recent years. Studies from different European countries reveal that the electricity consumption required for wastewater treatment can make up 1.0% of the regional energy consumption  with a sstewater ,3. The estewater , the enestewater , indicatC) and the amount of carbon that is incorporated into the excess sludge (CODES) in correlation to the SRT. The curves by Svardal and Kroiss [The energy demand for wastewater treatment with activated sludge is mainly driven by the sludge retention time (SRT) and the type of aeration system, whereby the energy required for the aeration can make up 60% of the total energy demand of a plant . Moreoved Kroiss  are based Kroiss  and can d Kroiss . Figure d Kroiss , the ene4-N levels in SDE are usually above 1000 mg/L. Since the nitrogen load in SDE can make up 20% of the incoming load, it is of high relevance to evaluate different SDE treatment concepts that can be realised on an individual WWTP.The biodegradation of raw sludge leads to the release of the incorporated nitrogen and in turn to high ammonium levels in the sludge dewatering effluent (SDE). NHSDE can be treated in mainstream or sidestream, i.e., directly in the activated sludge tank or in a separate tank, before it is recycled to the mainstream. Some plant concepts include a buffer tank that allows the storage and transfer of SDE into the activated sludge tank avoiding nitrogen peak loading. If there is no storage tank available, the SDE is directly returned to the activated sludge tank, where it increases the oxygen and energy demand for the aeration system significantly. The construction of dedicated treatment tanks for SDE represents an essential measure to relieve the aeration system in the biological stage and to create additional treatment capacities. A main benefit of a sidestream SDE treatment is an improved denitrification process due to a better nitrogen to carbon ratio. Since the amount of oxidised carbon is linked to the aerated tank volume, more readily biodegradable carbon is available for denitrification under lower oxygen supply. A side-effect of a reduced COD respiration under anoxic conditions is an increased adsorption of carbon to the excess sludge, which in turn raises the biogas yield during digestion. Depending on the type of SDE treatment applied in sidestream, the N to COD ratio in the influent of the mainstream tank can be also reduced in favour of denitrification.Biological mainstream or sidestream treatment options for nitrogen rich SDE are, e.g., nitrification/denitrification ; nitrita2/g NO3-N. Taking into account the biomass growth, the actual oxygen demand for nitrification amounts to 4.33 g O2/g NO3-N. Nitrification and denitrification are the conventional nitrogen removal concepts realised in the activated sludge tank (mainstream).Nitrification is a two-step conversion process of ammonium to nitrate. The first step of nitrification is the conversion of ammonium to nitrite (nitritation) by ammonium-oxidising bacteria (AOB). Nitrite-oxidising bacteria (NOB) are responsible for the further oxidation of nitrite to nitrate (nitratation). The oxygen demand for full nitrification (nitritation and nitratation) amounts to 4.57 g O2/g NO2-N), the required energy demand for aeration is approx. 25% lower than for nitrification. However, the pre-treatment by nitritation provides less oxygen in the form of nitrite for carbon removal under anoxic conditions in mainstream. Nitritation without a further conversion to nitrate can be achieved by suppressing NOBs, which are known to be more sensitive to high ammonium levels (free ammonia) and temperatures than the AOBs [4-N levels should not exceed 200 mg/L [SDE sidestream nitritation relies on the conversion of ammonium only to nitrite, whereby denitritation is usually performed in mainstream. Due to a lower oxygen demand for nitritation , 8% to nitrate and approx. 3% remain as ammonium in the pre-treated SDE. The total COD and nitrogen (TN) removal were assumed to be 95 and 80%. NH4-N removal was calculated with 100%.Further assumptions concern the microbial conversion within the different treatment processes. The conversion of ammonium in SDE sidestream is limited by the alkalinity, whereby the effluent after nitritation usually consists of 55% nitrite and 45% ammonium. In the case of nitritation and anammox, 89% of the treated ammonium is converted to nitrogen  are the same for all scenarios considered. The same applies to the excess sludge production during biological treatment, since all scenarios are based on a COD inflow load of 84 g COD/(PE·d) and a SRT of 15 d. During biomass growth, approx. 7% of the removed COD is incorporated as nitrogen into the biomass [el/m3 CH4). For this study, the CHP efficiency was assumed to be 30%.The primary and excess sludge are treated in anaerobic digesters, whereby the output after digestion was assumed to be 30 g COD/(PE·d) and 1.8 g N/(PE·d), according to the experience at real plants  and to modelling results . The metIncorporated nitrogen is released during digestion. Since the anaerobic degradation is the same, the nitrogen load in SDE is similar. Differences appear for the nitrogen load returned to the activated sludge tank, which depend on the applied SDE treatment concept. Thus, the energy demand for nitrification is driven by the nitrogen load after SDE treatment.2/kWh [2 provided with less efficiency (lower α-factor). The energy saving is much higher at two-stage plants due to the lower α-factor in the first-stage. The energy demand for the biological stage is linked to the efficiency of the aeration system. For tapwater, the standard aeration efficiency (SAE) of fine-bubble aeration systems is usually between 3.0 and 4.0 kg O2/kWh . The SAE2/kWh . The α-f2/kWh . Frey [22/kWh  reported4-N load to the activated sludge tank clearly decreased, resulting in a lower energy demand for aeration. However, the total energy demand for aeration in the separate SDE tank and the activated sludge tank was in total approx. 12.2 kWh/(PE·a), nearly the same for all scenarios considered. Although some publications claim a lower oxygen and thus a lower electricity demand for the anammox process [ process ,31, the In the second part of the study, different SDE treatment concepts were considered for the two-stage plant design. The nitrogen load in the SDE of the two-stage WWTPs was calculated with 1.7 g N/(PE·d), which corresponds to 21% of the incoming nitrogen load. Sidestream concepts (scenario 5 and 6) consider the return of the pre-treated SDE to the first stage of a two-stage WWTP, which significantly reduces the energy demand for aeration in the first stage due to denitritation. Additionally, the higher oxygen transfer rate in the sidestream tank has a positive effect on the total energy balance compared with the single-stage plant design. Overall, the energy demand for aeration of the two-stage plant is considerably lower compared with the single-stage WWTP operation (−9.3%). The lowest energy demand for aeration was found with 9.8 kWh/(PE·a) for the scenario with sidestream nitritation of SDE. Moreover, energy savings are possible with SDE sidestream nitritation and anammox. Compared with SDE treatment in mainstream, the SDE treatment with nitritation allows energy savings of approx. 8%; 3% of energy savings are possible for nitritation and anammox. The results of the two-stage plant design indicate that differences in the aeration energy of the SDE treatment concepts considered can make up 0.3 to 0.9 kWh/(PE·a). Since the specific energy consumption is usually in the range of 20 to 30 kWh/(PE·a), the total energy savings can account for 4.5% or more than 90 MWh/a for WWTPs larger 100,000 PE.2O emissions due to higher nitrite levels and loads. Especially sidestream nitritation was found to increase N2O emissions of a WWTP [2O is a climate relevant greenhouse gas, increased emissions affecting the CO2 footprint of a WWTP and should also be considered.A side-effect of the anoxic respiration and the low SRT in the first stage is an increased transfer of COD to the excess sludge with the benefit of higher biogas production in the digester. Compared with the single-stage WWTP operation (scenarios 1 to 3), the anaerobic stabilisation at two-stage WWTPs (scenarios 4 to 6) enables an overall higher gain of energy from anaerobic sludge stabilisation (+33%). The highest possible energy recovery from biogas was calculated with 19.9 kWh/(PE·a) for SDE sidestream nitritation, followed by nitritation and anammox with 19.4 kWh/(PE·a). The results indicate that sidestream SDE treatment allows an additional gain of energy from biogas up to 90 MWh/a considering a two-stage WWTP with a design capacity of 100,000 PE. Although SDE treatment in sidestream is attractive for saving energy and increasing the biogas yield, it was found in earlier studies that there can appear considerable Nf a WWTP . Since NMoreover, the calculations represented provide information about the carbon demand of the SDE treatment concepts. The lowest carbon demand for denitrification was found for the SDE treatment via nitriation and anammox with 3.16 kg COD/(PE·a), which is approx. 30% to 35% lower compared with the alternative scenarios. However, the carbon efficiency of the deammonification process presents no benefit, as the first stage needs to be operated under aerobic or anoxic conditions. Even though it is stated in many references that the deammonification of SDE reduces the energy demand of municipal WWTPs considerably, this is not the case for typical municipal wastewater where the oxygen demand for the carbon removal exceeds the oxygen demand for the nitrification. Moreover, there is an increased process control required in order to provide enough COD for denitrification in the second-stage. High N/COD ratios can restrict denitrification in the second-stage due to a lack of carbon and must be considered carefully.The results confirmed that the concept of two-stage WWTPs is advantageous for an energy-efficient operation with an increased energy self-sufficiency. Nitritation in SDE pre-treatment tanks and the use of nitrite to substitute oxygen in the first stage of a two-stage WWTP appears to be the most advantageous SDE treatment concept for saving aeration energy. Nevertheless, it should be mentioned that different process settings and operational conditions can enable another SDE treatment option to the more favorable concept for an individual plant. So far, publications reporting full-scale operation that allow verifying the results presented are scarce. Full-scale experiences from the implementation of a SDE sidestream nitritation on the second-stage WWTP Kirchbichl were reported by Ref. . The autThe goal of the study was to provide a comparison of different SDE treatment concepts by considering single- and two-stage plant design. The calculations performed by mass balances are based on specific data received from benchmark studies and full-scale experiences. Operators and engineers can use the described method to evaluate the best suitable SDE treatment concept for an individual WWTP by using plant-specific loads and data. The results presented demonstrated that the SDE sidestream treatment at single-stage WWTPs does not display an energetic advantage for one of the investigated treatment methods, since the energy demand for aeration was approx. 12.2 kWh/(PE a) for all scenarios considered. Moreover, the energy potential of the biogas was the same with 14.3 kWh/(PE·a). However, the deammonification process had a higher carbon efficiency with a 23% lower carbon demand compared with mainstream SDE treatment, which is a great benefit for single-stage activated sludge plants with high effluent requirements regarding nitrogen removal or a limited capacity of the aeration system. Overall, it was found that the two-stage WWTP operation allows an approx. 30% higher biogas production compared with single-stage WWTPs, since the anoxic respiration of heterotrophic bacteria and low SRT lead to a higher amount of COD transferred to sludge. This in turn reduces the energy demand for aeration considerably, whereby the energy demand for aeration is approx. 13% lower by considering the mainstream SDE treatment. SDE pre-treatment in sidestream via nitritation and the further treatment of nitrite-rich effluent in the first stage of a two-stage WWTP reduces the oxygen demand for COD respiration and is therefore the most favourable strategy to achieve an energy-efficient wastewater treatment with savings of approx. 8% of aeration energy.


Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 produced a global pandemic with significant mortality. As autopsies are not routinely performed on all decedents with SARS-CoV-2 infection, postmortem CT (PMCT) may be valuable to provide additional information on the cause of death and risk factors known to be associated with an increased mortality in COVID-19. The purpose of this manuscript is to review the PMCT findings in a series of 42 decedents with SARS-CoV-2 infection from our institution. Retrospective analysis of 42 decedents who had a positive postmortem nasopharyngeal swab for SARS-CoV-2 and had a PMCT were included in this study. Images were reviewed for pulmonary findings seen in COVID-19 and other organ involvement. Of the 42 decedents, although the majority had imaging findings in the lungs that would be consistent with COVID-19 and acute respiratory distress syndrome, in 14% of the decedents the SARS-CoV-2 infection was likely coincidental and the PMCT findings suggested that they died from other pathology. Over half of the decedents that died from COVID-19 had PMCT findings of vascular disease. PMCT is useful to identify pulmonary and extra pulmonary findings in decedents with SARS-CoV-2 infection that can provide additional information, which may be useful for the forensic pathologist to help determine the underlying cause of death.Supplemental data for this article are available online at Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became a global pandemic in March 2020 . PMCT haThe study was presented for review to the Olive View-UCLA Education and Research Institute- Institutional Review Board. The Institutional Review Board in the US is the governing body for the institution which oversees and regulates research. The study was exempt from review.Between March 1, 2020–December 12, 2020, 6 656 decedents were brought to the Los Angeles County Department of Medical Examiner-Coroner’s Office. The Los Angeles County Department of Medical Examiner-Coroner is mandated by law to determine and investigate the manner and cause of death in all violent, sudden, or unusual deaths occurring within Los Angeles County including all homicides, suicides, accidental deaths but also natural deaths where the deceased had not seen a physician within 20 days prior to the death. The Medical Examiner-Coroner’s Office can direct the forensic pathologist to perform autopsies and perform PMCT if necessary for the investigation of death.Retrospective review was performed of all decedents from March 1, 2020–December 12, 2020, that while being evaluated by the forensic pathologist had a PMCT and had a positive postmortem test for SARS-CoV-2. Forty-two decedents were included for review and all decedents had positive postmortem nasopharyngeal swab test SARS-CoV-2 confirmed by real time reverse transcriptase polymerase chain reaction (RT-PCR). The RT-PCR testing and PMCT examinations had been performed within 24 h of when the deceased arrived at the Department of Medical Examiner-Coroner’s Office. The request for the RT-PCR and PMCT were made by the forensic pathologist after evaluation of the clinical history and the unknown manner of cause of death and to investigate whether the cause of death was due to COVID-19.The decedents were scanned on a Phillips Brilliance 16 slice CT scanner  and images through the chest had been obtained without contrast on all cases. Image parameters for the chest was 120 − 140 kV, 150–300 mAs, 3 mm thickness. Additional imaging of the body had been obtained depending upon the individual clinical history by the forensic pathologist.The PMCT images were evaluated retrospectively by three Board Certified Radiologist  with 15, 15, and 10 years of radiology experience, respectively, and all with 3 years of PMCT experience. The radiologists were blinded to the official death record by the Department of Medical Examiner-Coroner’s Office. The PMCT images were reviewed initially individually and then consensus was obtained for the final interpretation.The PMCT images were reviewed using a viewing console . The PMCT images were evaluated for 1) pulmonary findings reported in the radiology literature known to be associated with COVID-19 infection including bronchial wall thickening, air bronchograms, heterogeneous ground glass opacification, crazy paving, and consolidation  pulmonar; (2) atyClinical history was obtained by the coroner investigators who are trained to gather information regarding the circumstances leading to death and pertinent medical history. The decedents’ race was noted. This was determined by the physical appearance, family members and how the decedent identified themselves. Height and weight were recorded, and body mass index was calculated. A body mass index of greater than 25 was considered overweight, over 30 was considered obese and over 40 was morbidly obese.n = 33), White , African American , Asian  and Native American . Majority  of the cases were either overweight , obese  or morbidly obese  (Supplementary Table S1).A total of 42 deceased that had a PMCT, and postmortem tested positive for SARS-CoV-2 were included in the study, 34 males and 8 females, with mean age 49.6 years (range: 19 − 89 years). Race or ethnicity were Hispanic  of the deceased in which the official death certificate recorded the immediate cause of death from COVID-19 had pulmonary findings on the PMCT that would be consistent with COVID-19 (Supplementary Table S2). The spectrum of the pulmonary findings in these deceased ranged from heterogeneous ground glass opacifications and/or consolidation and crazy paving with bronchial wall thickening, air bronchograms, air filled spaces to complete consolidation of both lungs (The majority (th lungs  and 2. On = 3) had non-pulmonary findings on the PMCT that may have contributed to the death . A small set of deceased (n = 3) lacked the pulmonary findings often seen in COVID-19 on the PMCT. The official death record determined primary cause of death as drug toxicity in two of the cases . In approximately 11% (n = 4) of the cases the heart was enlarged, and 20% (n = 7) had pericardial effusions. In two cases, pleural effusions could not be determined due to the complete opacification of the chest. In the remaining 33, approximately 33% (n = 11) had pleural effusions.Of the 35 decedents in which the official immediate cause of death was COVID-19, vascular disease with calcifications in the vasculature was identified in over half of the deceased  testing positive for SARS-CoV-2, PMCT in conjunction with the clinical history and external examination suggested that the immediate death was not due to COVID-19. Although our study sample is small, in our population with approximately 14% of SARS-CoV-2-postitive death, the COVID-19 diagnosis may be coincidental and not the cause of death. This further substantiates that PMCT can be a useful triage tool to determine if the death was due to COVID-19.In our study, we found that PMCT can provide useful information that can aid the forensic pathologist in determining whether there were additional contributing factors as to the immediate cause of death. In our study, not all deceased with COVID-19 appeared to have died from the disease. This is important as an accurate mortality rate from COVID-19 is critical to truly understand the disease burden. It is known that some individuals that test positive for the virus may be asymptomatic . DecedenIn our study, we found the pulmonary findings on PMCT in decedents who died from COVID-19 were similar to the findings on the limited number of autopsies that have been performed . HoweverOver 90% of the decedents in our study were either overweight, obese, or morbidly obese. This is consistent with reports that obesity is an independent risk factor for a poorer prognosis of COVID-19. Possible mechanisms attributed to the severity occur through higher ACE-2 concentrations, chronic inflammation and functional restrictive capacity in obese lungs . In contThe majority of our decedents with COVID-19 were Hispanic. This is consistent with Los Angeles County public health data as of July 4, 2021, in which the age adjusted death rates due to COVID-19 per 100 000 was greatest in Hispanic/Latino with a mortality rate of 364 per 100 000. This is in contrast to Asians at 162, African Americans at 211 and Whites at 124 per 100 000 . There aThe study is limited as there are only 42 cases for review, but to our knowledge, this is the largest cohort of PMCT in deceased who were SARS-CoV-2 positive. We believe that PMCT is a valuable tool in deceased with COVID-19. The data obtained through PMCT would otherwise be lost due to the current perceived risk of performing traditional autopsies in patients who are positive for SARS-CoV-2. As we try to gather meaningful data on the disease, we believe that PMCT can be a valuable tool in this process.Click here for additional data file.


Drosophila and find elevated morning rebound compared to evening. These effects depend on discrete morning and evening clock neurons, independent of their roles in circadian locomotor activity. In the R5 ellipsoid body sleep homeostat, we identified elevated morning expression of activity dependent and presynaptic gene expression as well as the presynaptic protein BRUCHPILOT consistent with regulation by clock circuits. These neurons also display elevated calcium levels in response to sleep loss in the morning, but not the evening consistent with the observed time-dependent sleep rebound. These studies reveal the circuit and molecular mechanisms by which discrete circadian clock neurons program a homeostatic sleep center.Homeostatic and circadian processes collaborate to appropriately time and consolidate sleep and wake. To understand how these processes are integrated, we scheduled brief sleep deprivation at different times of day in  The classic two process model posits that the circadian clock and the sleep homeostat independently regulate sleep . The cirWhile homeostatic drive persists in the absence of a functioning circadian clock , homeostDrosophila, a well-established model for investigating the molecular and neural basis of circadian rhythms and sleep. Sleep is characterized by quiescence, increased arousal thresholds, changes in neuronal activity, and circadian and homeostatic regulation  (+) subsets necessary for morning anticipation and Glu- necessary for evening anticipation under low light conditions (+ large LNv (+) DN1ps Drosophila" for further consideration by eLife. Your revised article has been evaluated by K VijayRaghavan (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Circadian programming of the ellipsoid body sleep homeostat in The manuscript has been improved but there are some remaining issues that need to be addressed, as highlighted by the reviewers below:1. The authors were asked to confirm that projections from Glu+ DN1p neurons innervate the anterior optic tubercle (AOTU), consistent with their model that a DN1p>AOTU>R5 circuit promotes rebound in the morning.Drosophila brain, and the region in which presynaptic puncta (and dendrites) of Glu+DN1p neurons are stated to innervate the AOTU (lines 227-228) does not correspond to the AOTU, which is located on the anterior of the brain adjacent to the ventral-most domain of the mushroom body α lobes  iScience, or Figure 3A from Omoto et al., (2017) Current Biology).In response, the authors provided confocal images in the new Figure 4A-C. However, the images show a posterior view of the The authors image is consistent with the work of Lamaze et al., (2018) Current Biology , who found presynaptic termini and dendrites of R18H11-neurons in lateral posterior regions of the fly brain. But I note that these authors also showed that R18H11-DN1p dendrites do not innervate the AOTU .2. Reviewers were in agreement that ablation of Glu+DN1p neurons does not cleanly show that these neurons account for differences in morning and evening rebound.For one, although not significant, the ablation flies showed a trend towards morning gain and evening decrease. Related to this, a concern is that the lack of significant difference (P ~ 0.09) in day/night rebound shown in Figure 4J arises because the study is underpowered.Given these concerns, we recommend that: (1) either you check the AOTU for innervation by taking anterior-focused confocal images that focus on the AOTU or revise your circuit model so it does not include the AOTU. (2) Tone down your conclusion about the role of Glu+DN1p neurons in morning rebound. Essential revisions:1. Time points examined (ZTs) vary from experiment to experiment and sometimes even within an experiment e.g. Figure 3C-D . In general, evening points vary from 8.5 to 10.5. These should be standardized across samples, unless there is a specific reason for not doing so.There is a specific reason for doing this noted in the second paragraph of the Results section with the following statement “To statistically compare morning and evening times of day here and throughout this study, we selected specific time points where the amount of sleep deprived and the baseline sleep during the rebound, two potential confounds, were comparable, allowing a direct comparison of sleep rebound.” Matching these variables required slightly adjusting the time points in some cases.To address this comment, we are providing supplementary figures  with additional time points that enable comparisons of the same time points (ZT1.5 and ZT9.5). In some cases, baseline sleep amounts now differ between ZT1.5 and ZT9.5 but sleep gain effects previously observed persist further validating our conclusions.2. There is no direct evidence that the changes observed in calcium levels or gene expression in R5 Ellipsoid Body neurons are of a circadian nature, and controlled by M and E neurons. Does disruption of M and E neurons affect these molecular and physiological rhythms? Are these rhythms maintained in constant darkness, or eliminated by mutations in clock genes? While it is not necessary to show circadian regulation of all parameters measured in the EB, circadian control should be examined for at least one.outClk mutant background using BRPSTaR  at ZT1.5 and ZT 9.5 . The time dependent difference observed in the WT background is not observed in the Clk mutant, thus we conclude that the observed time-dependent changes in the R5 neurons are dependent on the circadian clock.To demonstrate that time dependent physiological effects were due to the molecular clock, we tested if rhythmic levels of BRP were observed in the 3. When discussing for example figure 6e-f, it is said that both morning and evening sleep rebound is reduced, but it is not obvious that this is the case for the evening. Can the control and experimental flies be compared directly? This is actually a question that more broadly impacts the manuscript, as several claims are made that morning and/or evening sleep homeostasis differ (or not) from control, but there are no statistics to support those claims. Statistical tests are only run within genotypes to compare time points. To make additional claims, the appropriate comparisons need to be done.In order to address this point, we included panels for each claim that morning or evening homeostasis are changed compared to the controls and provide corresponding statistical analyses to support these claims. These panels are available in main figures 4 and 6 and the supplemental figures of figures 3 and 7.4. When manipulating neurons, only two time points are shown. Is it possible that the phase of rhythmic sleep homeostasis is altered rather than eliminated, and a rhythm is thus missed. As above, for at least one neural manipulation for each circuit tested, the whole cycle should be examined as in Figure 2.+ DN1ps) and evening (PDF- 5TH sLNv and LNds) circadian circuits respectively. In each case, we do not see significant evidence for a phase shift that mask an underlying rhythm by assaying only two time points in these strains.In order to address this, we include the whole cycle of scheduled sleep deprivation (SSD) in the supplements of figures 3,4 and 6 for broad clock neuron drivers and for the morning . The DN1p neurons presented in Figure 3A-B look more structurally similar to the ventrally projecting DN1p subpopulation – if so, this would yield a distinct circuit hypothesis. The authors can easily check this by co-staining with BRP and using higher resolution imaging to confirm the presence of DN1p axonal tracts or presynaptic sites in the intermediate lateral region of the AOTU  Current Biology, Figure 2B, D).To confirm that the DN1p population we selected is in fact an anterior projecting sub-population we labeled pre and postsynaptic regions of our DN1p split line  and co-stained for BRP . Our results indicate similar projection patterns to those observed in Lamaze et al. 2018, which is expected as that paper used R18H11-Gal4 and we used R18H11-DBD and thus we are likely labeling similar cell groups. Similar to the Lamaze 2018 paper we observed presynaptic puncta both in the pars intercerebralis (PI) and more modest numbers in the AOTU. We also note significant dendritic projections to the AOTU suggesting that DN1p neurons may receive input from the TuBu neurons also.7. Is it possible that differences in sleep architecture between morning and evening rebound period contribute to the observed differences in rebound? For instance, the evening consists initially of high anticipation, when there is little sleep to be lost during SD, and then high sleep during the first few hours of the night, which creates a ceiling for rebound; on the other hand, there is plenty of room for sleep increase in the morning. Could this create an inherent difference between morning and evening rebound levels?The authors are asked to at least discuss this issue.We addressed this by matching morning and evening time points in which baseline sleep (both during the deprivation and during the rebound) are not significantly different. For the evening time points, the sleep rebound period includes periods of high wake when sleep rebound can be observed. Indeed, when examined graphically using our 24h SSD protocol , one can see that rebound is observed initially but that this is quickly reversed.The manuscript has been improved but there are some remaining issues that need to be addressed, as highlighted by the reviewers below:1. The authors were asked to confirm that projections from Glu+ DN1p neurons innervate the anterior optic tubercle (AOTU), consistent with their model that a DN1p>AOTU>R5 circuit promotes rebound in the morning.Drosophila brain, and the region in which presynaptic puncta (and dendrites) of Glu+DN1p neurons are stated to innervate the AOTU (lines 227-228) does not correspond to the AOTU, which is located on the anterior of the brain adjacent to the ventral-most domain of the mushroom body α lobes  iScience, or Figure 3A from Omoto et al., (2017) Current Biology).In response, the authors provided confocal images in the new Figure 4A-C. However, the images show a posterior view of the The authors image is consistent with the work of Lamaze et al., (2018) Current Biology , who found presynaptic termini and dendrites of R18H11-neurons in lateral posterior regions of the fly brain. But I note that these authors also showed that R18H11-DN1p dendrites do not innervate the AOTU .We thank the reviewers for helping us clarify the issue. We have corrected the text accordingly including removing the DN1p-Tubu connection from our figure 5 and softened the language indicating such a connection.2. Reviewers were in agreement that ablation of Glu+DN1p neurons does not cleanly show that these neurons account for differences in morning and evening rebound.For one, although not significant, the ablation flies showed a trend towards morning gain and evening decrease. Related to this, a concern is that the lack of significant difference (P ~ 0.09) in day/night rebound shown in Figure 4J arises because the study is underpowered.Given these concerns, we recommend that: (1) either you check the AOTU for innervation by taking anterior-focused confocal images that focus on the AOTU or revise your circuit model so it does not include the AOTU. (2) Tone down your conclusion about the role of Glu+DN1p neurons in morning rebound.We have softened our language regarding the role of the Glu+ DN1p in morning rebound as suggested.