YouTube (YT) has become a popular health information reservoir. In this study, we aimed to evaluate the content and quality of YT videos as a source of patient information for meningiomas. A YT search was conducted for the following terms: “meningioma”, “meningiomas”, “meningeal tumor”, and “psammoma”. A total of 119 videos were examined by five independent raters, using validated quality criteria, including the Quality Criteria for Consumer Health Information (DISCERN), the Journal of the American Medical Association instrument (JAMA), and the Global Quality Score (GQS). The mean DISCERN score was 35.6 points, while the mean GQS and JAMA scores were 2.4 and 1.8, respectively. The majority of the videos were produced in the United States (37.82%). Moreover, 47.9% of the evaluated videos were graded as “poor” and only 9.24% were “good”. Statistically higher scores in all three scoring systems were associated with the following information: tumor localization, clinical manifestations, indications for surgery, treatment options, risks, adjuvant therapies, results, follow up, diagrams, and those that featured a doctor as the speaker. Misleading information was presented in 35 productions. Our findings show that the overall quality of YT on the topic of meningiomas is defective, and requires further improvement and evaluation. With YouTube (YT) currently being the most widespread, multinational video platform, with over two billion active monthly users of all ages, it is a simple and quick method to find information on any given topic. YT can be a learning resource for target audiences consisting of patients and their families, especially during the COVID-19 pandemic. Therefore, videos published on YT should be regularly checked, in terms of their quality, and improved, as they constitute a source of medical knowledge for a large group of recipients without medical education.So far, the quality of videos relating to neurological and neurosurgical conditions, such as migraine, glioblastoma, stroke or brain aneurysm, has been assessed ,2,3,4. OMeningiomas are the most common primary intracranial tumors in adults. Believed to be derived from meningothelial cells of arachnoid granulations of the arachnoid mater, meningiomas are slow growing and, in the majority of cases, benign central nervous system (CNS) neoplasms . The symOur aim was to evaluate the quality of YT videos about meningiomas for patients, their families, and other concerned individuals, using appropriately validated scientific scoring systems.A YT search was conducted on 10 April 2021 using a Google Chrome browser in “incognito mode”. A cleared search history, no Google account attachment, and ‘‘relevance-based ranking” was used to avoid personalization of the results. The following search terms were used for data extraction: “meningioma”, “meningiomas”, “meningeal tumor”, and “psammoma”. The first 75 findings were all included to achieve robust sampling. All researched data were publicly available, and the study did not include human or animal participation; thus, no ethics committee or YT permission was necessary.With the use of the above search criteria, a total of 300 videos were found. Duplicates were removed and videos in parts were treated as one production . Non-English, “pronunciation psammoma” videos and non-meningioma materials were excluded. Any material on brain tumors in general was assessed only on the part dedicated to meningiomas. The source of the upload , video duration, and substantive content of every video were analyzed. A comparable protocol of video content was extracted  according to previous studies ,9,10. InA team of five raters—one neurosurgery specialist, a neurosurgery resident, and three medical students in their clinical years—independently evaluated the videos using the DISCERN , GQS , and JAMA  scoring systems ,12,13.The DISCERN system is a validated instrument for evaluating the reliability of health information. It consists of 16 questions on various crucial characteristics of high-quality medical publications. For each of the questions, a video can score from 1 to 5 points . A score under 28 is classified as “very poor”, 28–38 as “poor”, 39–50 as “average”, 51–62 as ‘“good”, and 63–75 as “excellent”. Videos of “excellent” and “good” quality are considered to be a useful source of information and helpful for the patient to maintain high-quality data on the illness and treatment options; “average” videos are considered to be useful, but a patient would need an additional source of information, and “poor” and “very poor” videos are not considered to be useful and should be avoided by patients. The JAMA score ranges from 0 to 4 points, judging the following features of a publication: authorship, attribution, currency, and disclosures. The GQS is a 5-point scale (ranging from “poor” to “moderate” to “excellent”), evaluating the quality and flow of the video.(1)VPI = (likes × 100/(likes + dislikes)) × (views/day)/100;(2)Like Ratio = [likes/(likes + dislikes)] × 100.Introduced by Erdem and Karaca , specifiPQStat v.1.8.0  was used for statistical analysis . Data wek multiple raters was chosen and calculated according to the guidelines presented by Koo [The intraclass correlation coefficients (ICC) were chosen to compare the reliability between raters. Based on the dataset, a two-way random-effects model for d by Koo . SpearmaAfter applying the exclusion criteria, 119 videos were analyzed .n = 69), followed by hospital/clinic channels (n = 24), physicians (n = 16), and patients (n = 10) (p < 0.05). The largest number of videos were provided by health channels ((n = 10) . The DISp > 0.05). The United States was the country that uploaded the largest number of videos (n = 45). Other countries of video origin were as follows: India (n = 15), Australia (n = 2), Brazil (n = 1), Jordan (n = 1), Ireland (n = 1), Czech Republic (n = 1), and the United Arab Emirates (n = 1). In 52 cases, the country of origin was undefinable. Videos produced in the USA had statistically higher DISCERN/GQS scores in comparison with all other videos (p < 0.05). The differences in JAMA scores were not statistically significant.Other video providers did not show statistical differences between DSICERN results when compared (The video descriptive statistics are presented in p < 0.05); DISCERN/JAMA, r = 0.59 ; GQS/JAMA, r = 0.43 . From the 119 evaluated videos, 47.9% of the videos were graded as “poor”, 21.85% achieved “average”, 21.01% were “very poor”, and only 9.24% were “good”. Compared results of video grading with the used scoring systems is presented in The intraclass correlation coefficients (ICC) for consistency in GQS, JAMA, and DISCERN were 0.76 (CI: 0.68–0.82), 0.75 (CI: 0.67–0.81), and 0.7 (CI: 0.68–0.71), respectively. This indicates good and moderate reliability. The two-way random-effects model for k multiple raters was used . SpearmaThe mean DISCERN score was 35.6 (range 19.2–62), with a median of 33.8 (±9.2). The overall mean DISCERN score for each question (Q) was 2.73. For the GQS scale, the mean score was 2.4 (range 1–4), the median 2.4, and std 0.7. For JAMA, the mean score was 1.8 (range 1–4), the median 1.5, and std 0.7. The mean DISCERN scores for each Q are presented in The first two DISCERN questions (Q1 and Q2) were rated the highest (“are the aims clear?” and “does it achieve its aims?”), whereas Q12 (explanation of no treatment consequences) and Q7  achieved the lowest scores of 2.1 and 2.2, respectively.Data considering the video content for meningiomas are presented in p < 0.05, r = 0.4). Views, likes, subscribers, VPI, and the like ratio did not show any correlation with the DISCERN score. The DISCERN score was positively correlated with the duration of the video . Three videos on meningiomas during pregnancy had a mean DISCERN score of 46.7. The appearance of the above information did not statistically influence the scoring systems. One of these videos was classified in the top five by DISCERN, but none were in the top five VPI scores. When a patient was the speaker, this did not influence the DISCERN or GQS scores, but these videos had statistically lower JAMA scores  and WHO classification errors . One video presented an improper surgical technique. Nine cases of critical errors were detected, and are presented in p = 0.03). These data are presented in Animations were the most engaging factor, and they statistically increased views, likes, dislikes, comments, subscriptions, and VPI. Videos including advice on when to seek medical attention had a statistically higher number of likes. Productions containing misinformation resulted in a lower like ratio. The USA had a statistically higher VPI than other countries (All the data for the five videos with the highest DISCERN and VPI scores are presented in Neurosurgical topics on YT, such as hydrocephalus, glioblastoma, arteriovenous malformations, and brain aneurysms, tend to be of insufficient educational value ,4,9,10. The outcomes are comparable to a recent study that only evaluated videos on meningioma treatment . The aveThe tendency to post promotional videos, rather than educational videos, is becoming a noticeable trend with arguable ethics. Orthognathic posts on YT showed 100% patient satisfaction with their treatment and outcome . This waOur results should be considered as a guideline for future video providers on what essential information needs to be included in the material. Furthermore, broader verification should be performed, in order to identify when promotional material is disguised as “educational”. The amount of misinformation suggests the need for a comparable study in the next few years, to verify whether these errors have been corrected. Future studies should evaluate how YT viewers react to foul videos, and if this has a negative impact on the doctor–patient relationship.Our analysis only included English videos, with only the first 75 results for every search term. Moreover, in more than 50% of videos, the country of origin was undefinable, due to a lack of data on the video provider channel. Although many studies have tried to evaluate YT medical material, discrepancies in evaluation methods still exist . BrowserOur study results indicate that the overall quality of YT on the topic of meningiomas is insufficient and requires error correction. Much of the current information is misleading, and does not provide a sufficient amount of information for a non-medical viewer to fully understand the most common primary CNS tumors.


Traditional malnutrition screening instruments, including the Malnutrition Universal Screening Tool (MUST), strongly rely on low body mass index (BMI) and weight loss. In overweight/obese patients, this may result in underdetection of malnutrition risk. Alternative instruments, like the Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF), include characteristics and risk factors irrespective of BMI. Therefore, we aimed to compare performance of MUST and PG-SGA SF in malnutrition risk evaluation in overweight/obese hospitalized patients.2 vs. BMI ≥ 25 kg/m2.We assessed malnutrition risk using MUST (≥1 = increased risk) and PG-SGA SF (≥4 = increased risk) in adult patients at hospital admission in a university hospital. We compared results for patients with BMI < 25 kg/m2), 35% were overweight and 25% obese. Malnutrition risk was present in 16% according to MUST and 42% according to PG-SGA SF. In patients with BMI < 25 kg/m2, MUST identified 31% as at risk vs. 52% by PG-SGA SF. In patients with BMI ≥ 25 kg/m2, MUST identified 5% as at risk vs. 36% by PG-SGA SF. Agreement between MUST and PG-SGA SF was low (к = 0.143). Of the overweight/obese patients at risk according to PG-SGA SF, 83/92 (90%) were categorized as low risk by MUST.Of 430 patients analyzed (58 ± 16 years, 53% male, BMI 26.9 ± 5.5 kg/mMore than one-third of overweight/obese patients is at risk for malnutrition at hospital admission according to PG-SGA SF. Most of them are not identified by MUST. Awareness of BMI-dependency of malnutrition screening instruments and potential underestimation of malnutrition risk in overweight/obese patients by using these instruments is warranted. The impact of disease-related malnutrition on both patient outcomes and health care systems, e.g., in terms of care burden, hospital costs and length of stay, has gained increasing attention over the last decades –4. In reAlthough various tools are predictive of length of hospital stay, hospitalization costs and mortality, they use different criteria for risk assessment and therefore identify different patients –12. MostPrevious studies suggest that malnutrition or risk thereof may be easily overlooked, especially in overweight and obese patients, and that disease-related malnutrition may be predictive of worse patient outcomes irrespective of BMI –17. MoreObviously, there is a need for suitable instruments to identify patients at high risk for disease-related malnutrition, presenting with a different phenotype than low BMI or critical weight loss. Such an alternative is offered by the Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF) . This toIn the current study, we aimed to assess malnutrition risk in overweight and obese hospitalized patients using both the currently used MUST and the PG-SGA SF, and to compare the performance of both instruments for malnutrition screening on hospital admission across the BMI range.An observational cohort study was conducted among adult patients (age ≥ 18 years) admitted to four wards of a single university hospital, i.e., the University Medical Center Groningen (UMCG), in the period of March 2016 to July 2017, as described previously in other papers based on the same cohort –30. Two Malnutrition screening using the MUST  was partThe PG-SGA SF , 35 was 2 was calculated using extracted height and weight. BMI groups were defined by the WHO classification  [2) and overweight/obese BMI group (BMI ≥ 25.0 kg/m2) for further analyzes.Data regarding demographic characteristics, length of stay, and the patients’ height and weight were extracted from the hospital database. BMI in kg/m0 kg/m2)  and wereThis study was performed in accordance with the Declaration of Helsinki and was approved by the Medical Ethical Committee of the UMCG (METc UMCG 2016/106). Care as usual was provided during the study period, including dietetic treatment conform hospital protocol following national guidelines . In addit-tests for continuous variables , chi-square tests for categorical variables , and Kruskall–Wallis and Mann–Whitney U tests for ordinal variables . Cohen’s kappa was used to measure the level of agreement between malnutrition risk assessment by MUST and PG-SGA SF, both overall and dichotomized for BMI group, using the interpretation previously described by Landis and Koch  [Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 23.0. Data are presented as means with standard deviations for continuous variables, as medians with interquartile range (IQR) for non-normally distributed data and ordinal variables, and as numbers with percentages for categorical data. Differences in patients characteristics and malnutrition risk between BMI groups were tested for statistical significance using ANOVA with posthoc Bonferroni tests and independent samples reement) . Statist2) and 37% had a BMI within the normal range (18.5–24.9 kg/m2), 35% of the patients were overweight (BMI 25.0–29.9 kg/m2) and 25% were obese (BMI ≥ 30.0 kg/m2).Data of 965 individual patients were collected during the study period. MUST score was available in 679 patients, and PG-SGA SF in 723 patients. Complete data on both MUST, PG-SGA SF and patient characteristics on admission were available in 430 patients, as shown in Table p = 0.001), and there was a significant overrepresentation of women in the underweight group (p = 0.005). Height was similar across BMI groups, while weight and BMI differed significantly . There was no significant difference in distribution of patients across type of ward. Moreover, length of hospital stay was not significantly different between BMI groups.Age was significantly higher with increasing BMI (p < 0.001). According to MUST, all patients in the low BMI group (100%) had increased risk for malnutrition, which is inherent to how BMI is scored in the MUST. Prevalence of increased malnutrition risk assessed by MUST was much lower in the normal BMI group (26%), and very low in the overweight and obese BMI groups .According to MUST, 16% of all patients had increased risk for malnutrition. Distribution of risk of malnutrition assessed by MUST differed significantly between BMI groups (p = 0.001). According to PG-SGA SF, almost all patients in the low BMI group (91%) had increased risk of malnutrition. Prevalence of increased malnutrition risk assessed by PG-SGA SF was lower in the normal BMI group (50%), and in the overweight and obese BMI groups . Distribution of MUST score categories and PG-SGA SF score categories across BMI groups are visualized in Fig. According to PG-SGA SF, 42% of all patients had increased risk for malnutrition. Distribution of risk of malnutrition assessed by PG-SGA also differed significantly between BMI groups p = 0.00. Accordiк = 0.143, p < 0.001) was found. When stratifying data BMI into two groups , the slight agreement for the comparison of MUST score category vs. PG-SGA SF score category remained for both groups . Conversely, 90% (83/92) of patients with increased risk for malnutrition according to the PG-SGA SF, had low risk according to the MUST.In the comparison of MUST score category vs. PG-SGA SF score category, slight overall agreement between instruments (p = 0.007), had significantly lower body height (p = 0.001), had a significantly higher median hospital length of stay (p = 0.002), and were significantly more often admitted to a medical ward (p < 0.001) than overweight or obese patients with low risk for malnutrition. Patients with increased risk for malnutrition also were slightly older than low risk patients, but this difference was not statistically significant (p = 0.082). Body weight, BMI, and BMI classification were also not significantly different between overweight or obese patients with low or increased risk for malnutrition. Inherently, overweight or obese patients with increased risk for malnutrition had a significantly higher PG-SGA SF score compared to patients with low risk . The scores on all components (boxes 1–4) of the PG-SGA SF were also significantly different between patients at low or increased risk for malnutrition . The largest difference was found in Box 3 Symptoms (median score 5 vs. 0), followed by Box 4 Activities and functioning (median score 2 vs. 0) and Box 2 Intake (median score 1 vs. 0).Of the 258 patients with a BMI in the overweight or obese range, 36% had increased risk for malnutrition at hospital admission according to the PG-SGA SF. Overweight or obese patients with increased risk for malnutrition were significantly more often female  compared with previous studies performed in Australia and New Zealand   and IsraIn the PG-SGA SF, presence of nutrition impact symptoms largely contributes to the total risk score, in contrast to for example MST, MUST, or NRS 2002. In the current study, we found that overweight and obese patients at increased risk for malnutrition scored a median of 5 points on presence of nutrition impact symptoms, compared to 0 points in the patients with low risk for malnutrition. In patients with increased risk, this score indicates presence of two to five different nutrition impact symptoms, e.g., nausea, problems swallowing, diarrhea , 35. MonDifferent nutrition experts have previously advised to take caution with regard to BMI as a measure for nutritional status at the individual level, as BMI does not differentiate between body weight components. Assumptions on the stability of body weight components, specifically muscle mass and fluid status, are often not met in patient populations, and underlying muscle mass depletion may be masked by excess fat mass and/or fluid accumulation . RelyingPrevious studies have compared malnutrition screening outcomes to body composition assessment using gold standard methods for determining muscle mass, and underscore the importance of assessment in addition to malnutrition screening alone. In a study of 363 patients undergoing surgery for colorectal cancer, prevalence of increased malnutrition risk was 21% according to the MUST, while low skeletal muscle index (SMI) was present in 50% of patients as determined by CT. While there was a significant association between increased risk of malnutrition and presence of low SMI, 45% of the patients with low risk of malnutrition had a low SMI . SimilarIn the current study, we compared the performance of two previously validated screening tools across the range of BMI without using a gold standard method for the diagnosis of malnutrition and/or reduced muscle mass, which can be regarded as a limitation of our study. Although imaging methods, i.e., CT and MRI, are considered the gold standard techniques to assess muscle mass , 52, theOur findings should be interpreted with caution due to a lack of comparison with a “gold standard” comparator for the diagnosis of malnutrition, but point towards a multidimensional approach, in which other risk factors or facets such as nutrition impact symptoms, activity and function are more prominent components. As malnutrition screening is the first step in the nutrition care pathway and determines further assessment, intervention and monitoring actions , the choTo our knowledge, the current study is the first to compare malnutrition screening by PG-SGA SF and MUST, with specific focus on detection of malnutrition risk in patients with a BMI in the overweight or obese range. The use of data from clinical practice has provided us with more insight on this matter, however, this is accompanied by a relatively large percentage of missing data, resulting in potential bias. Another limitation of the current study is the lack of a “gold standard” for the diagnosis of malnutrition to test our findings against. Therefore, we cannot infer definite conclusions on which tool is “correct”, but our findings illustrate and raise awareness for the potential consequences of using BMI-dependent screening tools in an increasingly overweight and obese patient population. However, to put our findings in perspective, we are currently analyzing the predictive value of the current data for patient outcomes such as readmission and mortality. In a previous preliminary analysis of this data, we showed that increased malnutrition risk as assessed by PG-SGA SF was significantly associated with prolonged hospitalization, readmission, and mortality within 6 months after discharge, independent of age, sex, and hospital ward. Malnutrition risk by MUST was associated with higher risk of mortality, but not with prolonged hospitalization or risk of readmission , 30. TheIn conclusion, increased malnutrition risk is prevalent on hospital admission across the BMI scale, including more than one-third of patients in the overweight or obese range. Among the latter, only a small minority is identified by the currently used MUST. Alternative tools, less dependent on BMI, such as the PG-SGA SF, should be considered for initial malnutrition screening and additional nutrition assessment is recommended, to prevent malnutrition from being undetected and untreated in all patients, regardless of their BMI.


The sandwich principle is an educational concept that regularly alternates between collective and individual learning phases within one learning unit. Applying sandwich principle to lectures has proven to be more effective for learning outcomes than classical lectures. Supposedly, this teaching format also leads to a beneficial knowledge transfer when applied to other teaching formats. Therefore, the aim of this study was to investigate the effect of the sandwich principle on instructional videos and how its use was evaluated by students.n = 51) were randomly allocated into two groups. Both groups were given a test to assess the baseline level of knowledge. Afterwards, the control group watched the normal instructional video on cleft lips and palates, while the sandwich group watched the same video modified according to the sandwich principle. The participants then had to answer 30 single-choice questions to assess their knowledge gain and evaluate the instructional video. Long-term retention of the knowledge was tested again 6 months later using the same test questions. The unpaired t-test and ANOVA were used to compare the results.Participants (p < 0.0001). Regarding long-term retention, the mean test scores were still significantly higher in both groups than before watching the video (p < 0.0001). For all test results, there was no significant difference between the groups (p > 0.05). The evaluation showed that the students highly appreciated the modified video and found the interruptions for repetition of previously learned knowledge useful.Comparison of the pre-test and post-test results of both groups showed significantly increased test scores (The hypothesis that the modification of instructional videos according to the sandwich principle would lead to an improved learning outcome could not be proved subjectively or objectively. Nevertheless, the teaching format was highly appreciated by the students and may have increased their motivation to learn with instructional videos.The online version contains supplementary material available at 10.1186/s12909-021-02967-3. For several decades, teacher-centred methods of education have been used in medical education. The most traditional teacher-centred format is face-to-face teaching. It is defined as an instructional method in which a person teaches a group of students, for example, in a lecture hall. Although live interaction occurs between the learners and teacher, the teacher asserts control over the content and the way it is studied by the students. Generally, face-to-face teaching is set to a specific date and time ..However, student-centred learning approaches have increasingly been focused upon in medical education. Student-centred learning provides students autonomy and addresses individual learning needs and styles . It inclA teaching format that centres students in classical settings is called the sandwich principle. The sandwich principle is an educational concept that regularly alternates between collective and individual learning phases within one learning unit. During collective learning phases, students learn passively . This phase is supposed to be a compact mediation of knowledge with a maximum duration of 20-25 min, a timeframe that considers the length of the students’ attention span , 8. Durin = 252) were invited to voluntarily participate in this study. According to the curriculum, the students had no prior knowledge of the topic. The number of participants was calculated based on similar studies [All fourth-year medical students  watched the normal instructional video, while the sandwich group (group B) watched the same instructional video modified with activating elements according to the sandwich principle. Immediately after watching the video, participants had to answer 30 single-choice questions to assess their knowledge gain. Additionally, the tutorial video was evaluated. Long-term retention of the knowledge was tested again 6 months later using the same test questions. The need for ethics approval was waived by the institutional review board (EK 137/15). All methods were carried out in accordance with relevant guidelines and regulations for data protection.The topic of the instructional video was the aetiology and therapy of cleft lips and palates. The video covered incidence and aetiology, embryogenesis, classification, naso-alveolar-moulding-therapy, primary and secondary surgical therapy, and follow-up care. Overall, the video had a duration of 45 min, equal to the duration of a standard lecture or seminar.The instructional video modified according to the sandwich principle has two interruptions for the activating elements. The first interruption is at 10 min 18 s, and the second interruption is at 30 min 28 s. As activating elements, five tasks concerning the previously taught content had to be solved, including exercises such as drag-drop, matching, fill-in-the-blank, true/false and short answer questions. After the exercises were edited, participants were immediately informed about the correct answer.The duration of the control group’s learning phase was exactly as long as the instructional video. The sandwich group additionally had to edit the activating elements and therefore, their learning phase was about 10 min longer.Initially, the learning objectives for the e-learning programme were defined according to the SMART  criteria . A totalBoth groups evaluated the instructional video and carried out a self-assessment of knowledge acquisition before and after watching the video. Additionally, the sandwich group evaluated the activating elements in terms of usefulness, concentration, reflection of learning content, subjective difficulty level and future use. All aspects were evaluated using a 10-point Likert scale, where 1 denoted ‘fully agree/very good/too easy/appropriate’ and 10 ‘totally disagree/unsatisfactory/too difficult/inappropriate’ see .P ≤ 0.05 was considered significant. The effect size for discriminating between groups was estimated using Cohen’s d effect size and represented as d in the Results section. Values were defined as small (0.20–0.49), medium (0.50–0.79), large (0.80–1.29), and very large (above 1.30) [ppc2) was used to investigate the long-term effect [The obtained data were arranged using MS Office Excel 2019® . Statistical analyses were performed using GraphPad Prism 6 Software . All results were checked on normal distribution using the Anderson-Darling normality test. An unpaired t-Test was used to compare the results of the test within the groups and a 2 × 3-factorial ANOVA was used for comparison between the groups. For analyzing the results of the evaluation an unpaired t-test and the Mann-Withney-U test were used. ve 1.30) . Besidesm effect .n = 51, female 34, male 13, n.a. 4) were allocated into two groups, the control group  or the sandwich group . 19 participants were 19-22 years old, 15 were 23-26 years, 10 were 27 to 30 years and 4 were older than 30 years. 3 participants did not answer the question about their age.All participants (p < 0.0001)  Figs. . The conp < 0.0001). The mean score was reduced by 5.33 points in the control group  and 3.5 points  in the sandwich group. Still, the results of the long-term retention test are significantly better than the results of the pretest  ) and there was a small effect size (d = 0.15). Comparing the results of the posttest and the long-term retention test, it shows a large effect size (dppc2 = 0.847).The difference between the groups in the test results of the pretest, the posttest and the long-term retention test was statistically insignificant (p < 0.0001). There was no significant difference in the self-assessment between the two groups (p > 0.727) and a small effect size (d = 0.15). Evaluating the instructional video itself, both groups rated the video positively. Assuming that the scale midpoint constitutes a neutral rating, there was a significant difference in the sandwich group (p < 0.001).The results of the video evaluation and self-assessment of knowledge acquisition are shown in Table The sandwich group agreed that the interruptions were useful in helping their understanding of the educational content as they were driven to actively retrieve on previously learned information  = 1.67). Moreover, they agreed that the activating elements helped improve their attention and concentration . They found the interruptions useful, since the previously learned had to be reflected . The difficulty level of the activating elements was assessed rather difficult . The group found that the interruptions were placed appropriately throughout the video . Most students agreed that they could imagine learning in the future using instructional videos that are modified according to the sandwich principle .The sandwich principle has previously been applied successfully to lectures. In this teaching model, it has been proven that the application is more effective and leads to a significantly better learning outcome compared with classical lectures . This stA comparison of the results of the posttest and long-term retention test to the baseline level of knowledge revealed that both groups had significantly better results after watching the instructional video both times. When only the results of the posttest and the long-term retention test were compared, both groups showed a decrease in their mean score. Although this decrease was significant in both groups, the results of the sandwich group were slightly better than those of the control group. This outcome might indicate a beneficial long-term effect for the modified instructional video owing to the activating elements. This long-term effect could be analysed better through with an increased testing scope, a different testing format or a different choice of interval for testing long-term retention ..In addition, there was no significant difference in the test scores between the sandwich and control groups. Therefore, the interruptions for the activating elements did not induce the desired testing effect, retrieval practice to boost the long-term learning . MultiplThe results of the self-assessment of knowledge acquisition likewise show that the students assessed their own knowledge equally good before and after watching the instructional video. Therefore, the objective and subjective results enable us to assume that the instructional video itself must have a good teaching effect. This notion is confirmed by the evaluation of both groups, who verified the advantageous didactic effect of the video itself. The hypothesis that the modification of instructional videos according to the sandwich principle leads to an improved learning outcome could not be proven. Therefore, whether the extra work of developing activating elements and editing the video for the modification is worth the effort must be discussed.The evaluation showed that the students in the sandwich group highly appreciated the modified video. They found the interruptions useful for repetition of previously learned information. The participants also confirmed that the interruptions improved their concentration and attention to the video. They pointed out that they would like to learn in the future using instructional videos modified according to the sandwich principle. In general, the use of multimedia is highly appreciated by students and can be a powerful supplement and motivator to classical teaching formats –25. A reFrom the teachers’ point of view, applying the sandwich principle to instructional videos requires a great deal of work. Besides setting a special focus when establishing the learning objectives, the activating elements have to be created and integrated into the video. These preparations are time consuming, especially under the aspect of an equal beneficial learning outcome. Nevertheless, once created, the modified instructional video is a sustainable teaching method that offers students a flexible, asynchronous study method by being independent of time, place and speed –30. Alth.In the present study, the length of the instructional video (45 min) can be regarded as a limitation. In general, shorter instructional videos are better for the attention span. Shell et al. found that the optimal length of instructional videos is 5 - 10 min . AccordiThe hypothesis that the modification of instructional videos according to the sandwich principle leads to an improved learning outcome could not be proven subjectively and objectively. Nevertheless, the teaching format is highly appreciated by the students and may increase their motivation to learn with instructional videos. Therefore, instructional videos modified according to the sandwich principle are a possible option when transitioning from traditional to digital teaching.Additional file 1.


Ensuring the security of modern cyberphysical devices is the most important task of the modern world. The reason for this is that such devices can cause not only informational, but also physical damage. One of the approaches to solving the problem is the static analysis of the machine code of the firmware of such devices. The situation becomes more complicated in the case of a Smart Home, since its devices can have different processor architectures (means instruction sets). In the case of cyberphysical devices of the Smart Home, the destruction of machine code due to physical influences is also possible. Therefore, the first step is to correctly identify the processor architecture. In the interests of this, a machine code model is proposed that has a formal notation and takes into account the possibility of code destruction. The article describes the full cycle of research (including experiment) in order to obtain this model. The model is based on byte-frequency machine code signatures. The experiment resulted in obtaining template signatures for the Top-16 processor architectures: Alpha, X32, Amd64, Arm64, Hppa64, I486, I686, Ia64, Mips, Mips64, Ppc, Ppc64, RiscV64, S390, S390x and Sparc64. Information security, the relevance of which in the modern world is unconditional, affects many areas that relate not only to countering relevant threats, but also to the study of their consequences ,2,3. So,Of particular criticality is the security of cyberphysical Smart Home devices, which actually produce physical influences under the influence of software in the human environment. Thus, violations of information security during the operation of these devices can have serious consequences for the life and health of people . MoreoveThe investigation of cybercrimes is dealt with by the corresponding science–forensics . Its maiOne of the initial stages of forensics, obviously, consists of collecting information about the system, both the one at which the threat was directed (the victim) and the one from which the attack was carried out (the offender). Most attacks on information resources are carried out using executable files . The study of the machine code (hereinafter–MC) of executable files will allow one to understand the logic of their work, which, hypothetically, will make it possible to detect malicious code .There is a sufficient number of methodological approaches and methods for the analysis of MC ,10,11. HParticular difficulty manifests itself in the analysis of MC of cyberphysical devices with various functional purposes. This follows from the fact that, depending on the purpose of the device, one can choose its own Architecture, adapted to the necessary tasks for the given requirements. For example, some cyberphysical devices require high computational efficiency (despite the resources spent), others demand low power consumption (at the expense of performance), and others claim high-quality computations (in the absence of time constraints).Based on the designated aspects of the use of cyberphysical devices, including devices used for the functioning of Smart Homes, and the large heterogeneity of Architectures used in cyberphysical devices, as the current research area of this article, the study of the Architecture of CyberPhysical Devices of a Smart Home (CPDoSH) is highlighted . There atheoretical—having an analytical form, suitable for the entire set of CPDoSH MC;practical—allowing a demonstration of specific parameters (and characteristics) for the given CPDoSH Architectures.To define the CPDoSH Architecture, it is required to develop an CPDoSH MC model , which will allow creating algorithmic support for the corresponding identification method and its implementation. The model must be described in two forms:Obviously, to obtain a practical form of the developed model, a number of experiments will be required.Thus, the main scientific result of the study, naturally possessing scientific novelty, is the analytical MC Model, adapted to identify its architecture. At the same time, the model allows one to analyze in a formal form the various operations of code destruction, that is relevant for the area of CDPoSH. This Model differs from the existing ones in the degree of detail and mathematical rigor of the description.The article also poses and proves a hypothesis regarding the possibility of identifying an MC by its byte-frequency signatures. The novelty of the study is also confirmed by the fact that in the process of this proof, template Signatures for Top-16 Architectures are generated (according to the file sets from different assemblies of OS Gentoo).This study is methodologically holistic for the following reasons:First, the analysis of the subject area in terms of the CDPoSH MC metadata is carried out with a hypothesis about the identification of the Architecture. As a result of this analysis, a CDPoSH MC Model is formed.Secondly, the work synthesizes a research software tool that allows one to check the validity of the hypothesis for identifying the Architecture.Thirdly, a full-fledged experiment is carried out using both the resulting Model and the developed software. The results of the experiment make it possible to substantiate the hypothesis put forward.The theoretical significance of the work lies in building an MC model for a set of common architectures, taking into account the destructive actions characteristic of the area of CDPoSH. The problem of identifying the CDPoSH Architecture is also formalized in terms of the constructed Model.The Model allows the use of formal tools for program analysis, putting forward hypotheses, building private  models of programs, and so forth. With the model, the process of code destruction is also characterized, and its basic typing is also given.The practical significance of the work lies in the construction of specialized MC signatures for a set of common Architectures that are also used in CDPoSH. These signatures can be considered invariant from the purpose of the code, its size, and programming language.A signature can be considered invariant with respect to more abstract characteristics of programs, such as development language, degree of optimization, functionality, and so forth. As a consequence, it is possible to collect a complete database of signatures for all existing types of processors.The overall contribution of the study is aimed at developing tools for investigating cybercrime.At the same time, without identifying the processor architecture (considered in this investigation), most methods of subsequent analysis will not be applicable.The structure of the article follows the sequence of research stages and is as follows.We will review scientific works devoted to various methods aimed at classifying files, analyzing program code, detecting data areas with MC, and identifying its characteristics. All these results can be applied for the main investigation task—the analytical MC Model creation in the interests of the subsequent CPDoSH Architecture identification.General approaches to the extraction of various metainformation from binary code have already been reflected in a number of scientific studies.In , the ProIn , it is pIn  the secuIn , a new EIn , the proIn  it is prIn , the infThe Work  is devotIn , an apprRef.  outlinesThe solution given in  makes itIn , a methoIn , ELF proIn , a methoIn , the posThe work  is devotIn , the mosIn , to deteIn , it is pAccording to the review, only a small number of scientific articles are devoted to the issue of file classification, and even fewer—to the identification of the Architecture applicable to CPDoSH. Most of the existing work (partly included in the current review) is aimed at detecting malicious software, that is, solves a secondary problem when the Architecture is known and the necessary tool for working with it is selected. The works on the identification of the Architecture itself, although they describe promising ideas, nevertheless do not contain a comprehensive study on the subject of the final effectiveness. Thus, the scientific and technical problem stated in the current article is undoubtedly urgent. The problem does not have a full-fledged solution, and has not been studied from various aspects of practical application.Based on the review, one can indicate the following prerequisites for solving the current problem. First, the analysis of files from CPDoSH firmware is quite efficient in ideal situations—for files without damage and with the correct header. Therefore, an attempt to disassemble “real”  programs for different processors will have a high level of errors (both I and II) and will require manual validation of the results. Secondly, the comparison of the hashes of the files under study  with the reference database is efficient only if the latter is fully available. At the same time, due to the sufficient novelty of the Smart Home applications, such bases for CPDoSH are not yet sufficiently formed. Thirdly, a solution using byte-frequency signatures can be considered the most effective, although it may not be sufficiently resistant to the MC damage (which will require verification). This situation is especially relevant for CPDoSH, which are unlikely to have protection against vandalism or accidental damage, since they are consumer products, the price reduction of which is achieved in all possible ways. Fourthly, the applicability of machine learning methods in most works unconditionally speaks about the justification of this area and for solving the current problem. It would also be logical to assume that the artificial intelligence methods underlying the functioning of Smart Homes should also be aimed at ensuring the security of its cyberphysical devices.using the minimum number of parameters in the Model;full automation of work with the Model;simulation of MC, which can be protected from analysis and/or be partially destroyed;extensibility and adaptability of the Model for use in intelligent systems .Thus, it is interesting from a scientific and practical point of view to develop a solution for creating a Model suitable for the subsequent identification of the CPDoSH Architecture. Moreover, the Model must have the following characteristics:Based on the general lack of research specifically in this subject area, it is necessary to first present the terminological basis for the identification problem of the CPDoSH Architecture, with which the current research will be described. In the interests of this, the following domain ontological model was developed (not related directly to the Model created in the current research).The diagram of the ontological model of the subject area, which defines the basic concepts and their relationships, is shown in CyberPhysical Device of a Smart Home (or CPDoSH)—a device, which interaction with the physical world is determined by the program code  running on its processor;File—information object of the CPDoSH storage; can be of various types that determine its purpose and data structure;Program—a type of the file that contains program code ready for execution in CPDoSH, as well as a header that defines meta information about the program;Header (programs)—an area with metadata at the beginning of the program, defining its structure  and launch parameters ; in the current context, two types are considered: ELF—for programs of the Windows OS family, PE—for programs of the Linux OS family ;Section—an area with a lot of the same type of binary data contained in the program; in the current context may include MC and data for the operation of its algorithms;Machine code—program code  contained in the code section and ready for execution by the CPDoSH processor;Data—information contained in the data section and used by the CPDoSH processor  when executing the MC;Processor—an electronic unit (or integrated circuit) of CPDoSH that executes MC instructions; is an implementation according to some Architecture;Architecture (processor and machine code)—a formally given set of MC instructions executed by a family of CPDoSH processors;A class of machine code—a set into which an MC is divided, referring to one CPDoSH Architecture;Identification (Architectures)—the process of defining a specific type of CPDoSH Architecture; classically using a special field of the program header, and in this context using the MC program;Model (machine code)—a formalized description  of MC, suitable for its identification; is the main scientific result of the research described in the article;Particular signature or just a signature (machine code)—the byte-frequency allocation of the MC of a single program for CPDoSH;Template signature (of Architecture and MC class)—byte-frequency allocation of MC of a certain class, collected from a fairly large number of programs of this CPDoSH Architecture; sufficiency in this case is determined by the fact that the Signature remains unchanged when new programs are added to the allocation; is a generalization of the frequent Signature;Byte-frequency allocation (of machine code)—allocation for binary recording of MC in the form of the number of bytes of each from 0 to 255 values, divided by the number of bytes of the maximum value; reflects the distinctive features of MC of a program of one Architecture from other programs and Architectures of CPDoSH; obtained from the Model and defines the Signatures of the Architectures (both particular and template);Research prototype —a software tool prepared in the framework of the current research, which builds the byte-frequency allocation of a given program; allows one to calculate template signatures in this way.Let us introduce the terms reflected in the domain model see  in the aThis ontological model will allow one to avoid terminological confusion in the further consideration of the proposed approach and ongoing experiments, as well as to create a deeper understanding of this subject area.Using the introduced terminological basis, we will describe the stages of the research described in the article.Stage 1 analyzes the MC structure corresponding to programs running on the CPDoSH. The metadata highlighted in the MC allows constructing its “ideal” Model in an analytical form. The term ideal Model implies its applicability to the case when the MC was not subjected to both accidental and deliberate changes.In terms of the Model, the following (target) research task is posed—the direct identification of the CPDoSH Architecture.At Stage 2, the ideal Model is specialized for cases of MC destruction, which is especially important in the case of CPDoSH. To substantiate this, a real example can be given, when, as a result of an error in the heating subsystem of a Smart Home, there will be a critical temperature rise, which will lead to a fire . As a reThen, a final analytical Model is proposed that reflects the CPDoSH MC even in the event of its destruction of any degree.At Stage 3, the concept of an MC signature is analytically set, which allows one to give a more formalized record of the procedure for identifying the Architecture by the Model.Then a hypothesis is put forward regarding the possibility of “distinguishing” Architectures using the MC Signature. The proof of the hypothesis substantiates the feasibility of the proposed of the CPDoSH MC Model (and subsequent identification of the Architecture).At Stage 4, a research prototype for calculating the byte-frequency allocation of the CPDoSH MC (hereinafter—Prototype) is being developed. The Prototype architecture is beyond the scope of the current article solely dedicated to MC modeling. The prototype, on the other hand, is only an auxiliary tool.In Stage 5, using the Prototype, a series of experiments is performed to calculate the byte-frequency allocation of the OS Gentoo assembly for Top-16 Architectures. Each of the Architectures can be applied to create the CPDoSH for various purposes, working conditions and required characteristics. For example, the embedded operating system eCOS  runs on As a result of experiments, the 16 byte-frequency signatures of the MC classes are practically formed. Each of the signatures statistically reflects a feature of a particular Architecture and can be used to directly identify them.Let us describe further the progress of all 5 stages of the current research.Let us analyze the structure of the MC of a program for CPDoSH and metadata (as information about the code itself), applicable to identify the Architecture. So, each program has a header of a certain structure with the information necessary for loading and execution. The most popular examples of headers are ELF (Executable and Linkable Format) and PE (Portable Executable); both define the way a program is displayed in memory, and are used in different operating systems .This header structure stores the following information, the most interesting from the point of view of MC identification. First, the structure contains directly the Architecture itself, on which the execution of the MC of the program is supposed. This field cannot be used in all 100% of cases—thereby completely solving the scientific and technical problem and achieving the research goal. The reason for this will be explained further. Secondly, sections with data are indicated in the structure, some of which are related to the executable code (sections are named as “.text”). The instructions of the processor, which architecture must be determined, are located in this section. Thirdly, modern programs can be 32-bit or 64-bit, which determines the bit depth of data and processor instructions for working with them. The corresponding field of the program header structure is intended for this. Fourthly, since most instructions consist of several bytes, their order in memory matters, which can go from high to low  and vice versa—from junior to senior (denoted as little-endian and indicated on purpose). This is defined by another header field. It should be noted that, despite the seemingly obvious use of information about the Architecture from the header of the program for identifying the MC, this approach will not always work, and that is why the construction of a universal Model (according to the task of the current research) is relevant.The inapplicability of using a header for identification is as follows.Firstly, in a number of cases the Architecture in the header can be deliberately replaced with an incorrect one—precisely for the purpose of complicating its analysis during reverse engineering. This is justified by a large number of commercial CPDoSH developments, the uniqueness of the applied solutions, high competition in the field, which leads to the desire of developers to keep their know-how. At the same time, such a “substitution” will not affect the very performance of the program, since the MC will be executed on a specific CPDoSH, which has a known processor in advance.Secondly, as a result of third-party factors , the program can be destroyed , which will simply lead to the “incorrectness” of this field of the header structure .Thirdly, the problem of identification is somewhat beyond the scope of a simple study of a full-fledged program, since for the CPDoSH it is an analogue of an embedded microprocessor system, which potentially does not even contain an Operating System. Consequently, for analysis, there can be an MC contained in another data set (even executed in memory) without a full-fledged program header. To analyze the same “raw” data in order to identify a malicious MC, you will first need to define its Architecture.N—the number of sections; i-th section (j—a index of bytes array); The result of the analysis of the structure of the program MC was the allocation of the following 4 types of metadata: Architecture, sections with MC, bit width and byte order. This metadata can be written in the form of Equation .(1)ModeThen the task of defining the Architecture  becomes trivial, described using the Equation (2)ClassThe given Model fully reflects the MC, and the task of defining its Architecture is also written in an analytical form. However, neither the Model nor such identification can be fully applied for the CPDoSH, since (as emphasized earlier) the Smart Home device program differs from classical programs . Thus, MC in CPDoSH can be subjected to destructive changes that will not be reflected in the ideal Model (will not be taken into account).Algorithm 1: Pseudocode for Stage 1 algorithmThe main actions of Stage 1 are presented using Algorithm 1 (in pseudocode form).Algorithm 1 receives many variations of MC  In line 8, the algorithm returns the generated Line 9 ends the algorithm.k destruction, ∅—the designation for converting the header field to an incorrect value.In the case of the destruction or absence of the program header , specialization (or refinement) of the Model will be required. Such a new Model of the CPDoSH MC (denoted without a apostrophe) can be written by applying to the old  with using of Equation .(3)ModeEquation .(4)DestSo, the first type of destruction corresponds to the situation of deliberate change of the CPDoSH Architecture in the program header to the wrong one. The second type means the absence of a header with a clearly defined range of MC instructions (since the sections are saved). The third type is identical to a set of some data potentially containing MC, the identification of which must be carried out. If in the case of the destruction of the first and second types, the information about the sections remains, and, therefore, the bytes of MC for identification can be used, then in the case of the third type it will not even be possible to select the MC, since it will “mix” with the data other sections . However, there are techniques for detecting MC in a dataset, which will allow solving this problem of allocating bytes of MC .N and Based on this, despite the type of destruction of the program, the final Model of the MC can be written in the form of a set of bytes of its instructions (see Equation ).(5)ModAlgorithm 2: Pseudocode for Stage 2 algorithmThe main actions of Stage 2 are presented using Algorithm 2.Algorithm 2 receives a Model for an ideal situation .(7)FreN—number of sections of the program with MC; i-th section; j-th byte in the i-th section.The procedure for determining the Class using the identification procedure can be written using Equation .(8)ClasProceeding from the set goal and the formalized MC Model, the research task is reduced to the creation of a method for comparing the Signature of a sample of the CPDoSH MC and its Hypothesis 1.Many machine code files running on a single processor architecture have their own unique frequency signature that is different from the rest.Hereinafter, for simplicity and in the interests of the future experiment, we will assume that each copy of MC (possibly consisting of many sections) is stored in the CPDoSH in a separate file.F—the number of Models of this Class; moreover, the Signature of one This hypothesis can be formally written using Equation .(9)SignAlgorithm 3: Pseudocode for Stage 3 algorithmThe main actions of Stage 3 are presented using Algorithm 3.Algorithm 3 receives a Line 1 starts the algorithm.In line 2, the In line 3, on the basis of the task (“convert (→) Line 5 puts forward a Hypothesis based on the introduced concept of Line 6 converts the In line 8, the algorithm returns the advanced and formalized Line 9 ends the algorithm.To verify the Hypothesis, it is necessary to construct the averaged byte-frequency allocations of MC of all classes, that is, the Class Signatures of the CPDoSH MC. In the interests of this, a research prototype was developed, adapted to work with the CPDoSH MC. Although the description of the prototype and its basic testing is beyond the scope of the current work on modeling of MC, we will give brief information about the Prototype.The prototype is a console software tool that runs in the OS Windows environment.For the development of the Prototype, the Microsoft Visual Studio environment (version 2019) was chosen, which is one of the undisputed leaders in the field of IT engineering. Including for this reason, the development language was C# (obviously supported in the environment).Despite the high and constantly growing popularity of the Python language, especially when developing programs using machine learning, it was not applied for a number of the following subjectively negative signs: focus on ease of writing code, which for programmers with more experience rather complicates development, because it requires memorizing new  “simple” constructions; “Related proximity” of C# and Microsoft Visual Studio, which is naturally reflected in the use of the former in the latter ; negative author’s experience of using scripting languages  versus those built on “pure” bytecode , as well as the results of independent studies that show higher speed and less resource consumption of the second group of languages compared to the first, which also influenced the final choice.The main purpose of the prototype is to build byte-frequency signatures based on OS Gentoo assemblies for a set of Architectures.Mode 1 (preparatory): unpack the OS Gentoo build image into a separate directory for further processing);Mode 2 (optimization): deleting from unpacked (after Mode 1) files that are not involved in the construction of signatures, since they are not programs;Mode 3 (main): processing of MC in unpacked (after Mode 1) and optimized (after Mode 2) files contained in programs, and construction of template Signatures for the CPDoSH MC by their bytes.The prototype works in the following 3 modes:MCArchIdent.exe is the name of the application executable):for Mode 1:> MCArchIdent.exe UnpackMode ArchiveFile UnpackDirUnpackMode is an indication of the current operating mode, ArchiveFile is the path to the archive file for unpacking, UnpackDir is the path to the directory for unpacking the archive;where for Mode 2:> RemoveMode SomeDirRemoveMode is an indication of the current operating mode, SomeDir is the path to the directory for removing unnecessary files (except for ELF and PE programs);where for Mode 3:> MCArchIdent.exe SignatureMode SomeDir SignatureFile ElfArId PeArIdSignatureMode is an indication of the current operation mode, SomeDir is the path to the directory with programs for building a template Signature; SignatureFile—path to the file for saving the template Signature; ElfArId—a numeric identifier of the Architecture to check in the ELF program header; PeArId—a numeric identifier of the Architecture to be checked in the PE header of programs.where The Prototype is a console application with the following startup argument format :As a result of execution, the following log will be displayed  a template signature will be built from the The above testing shows the basic performance of the Prototype.Algorithm 4: Pseudocode for Stage 4 algorithmThe main actions of Stage 4 are presented using Algorithm 4.Algorithm 4 at the input receives the previously advanced Line 1 starts the algorithm.From lines 2 to 9, using the fields of the Line 2 specifies the development Line 3 specifies the Prototype Line 4 specifies the integrated development environment (Line from 5 to 9 indicates the main steps of the Prototype (written in pseudocode as a text list of subtasks).Line 11 creates a Line 12 puts forward the basic Line 14 checks the results of testing the Prototype : Alpha,big endian; however, as noted, the byte order should not affect the Signature, since the byte order remains the same.It should be noted that on the official OS Gentoo site there were assemblies of other variations of these Architectures . However, as a preliminary study has shown, such variations have a sufficiently high proximity of byte encoding of instructions, which does not allow using their Signatures for accurate identification. Therefore, they were excluded from further consideration. Such variations include assemblies for Arm with different byte order and hardware support for floating point or Mips with 32 and 64 bit for different byte order, as well as HPPA for different bit order. However, this is not a disadvantage, since the Signature of any variation of this Architecture is significantly different from the Signatures of other Architectures. With equal variations of assemblies of two bits with the proximity of their Signatures, it was 64-bit that was taken, as reflecting the modern trend in the development of information technologies to increase the size of the processed data. In the case of variations for different byte ordering, the most popular was taken—Alpha—for Alpha (only 64-bit);X32—for 64-bit AMD when working with 32-bit numbers;Amd64—for 64-bit AMD;Arm64—for 64-bit ARM;Hppa64—for 64-bit Hewlett Packard (with Precision Architecture);I486—for 32-bit Intel up to Pentium Pro and II;I686—for 32-bit Intel for Pentium Pro, II, etc.;Ia64—for 64-bit Intel Itanium;Mips—for 32-bit MIPS;Mips64—for 64-bit MIPS;Ppc—for 32-bit PowerPC;Ppc64—for 64-bit PowerPC;RiscV64—for 64-bit RISC-V;S390—for 32-bit IBM System;S390x—for 64-bit IBM System;Sparc64—for 64-bit SPARC.A brief transcript of the given Top-16 Architectures , is as follows:Each of the list of Architectures corresponds to the Class, which must be identified by the MC CPDoSH.All OS assemblies, except for Mips, had the Each assembly looked like a compressed TAR container (containing other nested containers) and sizes from 157 MB (for Sparc64) to 324 MB (for Alpha). Such a spread in the sizes of assemblies  can be explained by the different number and size of files included in the composition that are not related to programs with MC. The overwhelming majority of programs within assemblies were in ELF format.In addition to the significant size of the sample , it can be considered quite diverse in terms of all possible variants of Signatures, since Gentoo is an OS and, therefore, contains the implementation of a fairly wide set of functionality. The extraction of files from each assembly was carried out by another module, also included in the Prototype.The name of the OS Gentoo assembly file and their sizes, as well as the sizes of the MC obtained from the sections of all the extracted executable files for each of the Top-16 Architectures, made up the values given in Following Using the Prototype sequentially in 3 modes for each of the OS Gentoo assemblies will allow obtaining Signatures of 16 MC classes, suitable for identifying of CPDoSH. Such a set of Signatures represents a Model of the CPDoSH in a practical form, specialized for one Architecture from the Top-16.The resulting MC CPDoSH Class Signatures are shown in A visual analysis of the graphical representation of histograms for the Class Signatures of the CPDoSH MC see  allows uFirstly, the Signature of each Class has a clearly distinguished peak of the maximum height at a byte value of 0, which is explained by the use of this value as the standard in many cases—for example, during initial memory initialization.Secondly, each Class is characterized by a high frequency of occurrence of a byte with a value of 255. It is apparently due to its frequent use for service needs; for example, a value of −1 is corresponding to the unsigned form of writing the number 255.Third, the frequency for the value 0 is about 2–5 times higher than the frequency for the value 255, which can be explained by the general ratio of the use of these numbers in the program code.Fourthly, 32-bit and 64-bit Architectures that are quite similar in their principles of functioning, differing only in the size of the data being operated , have different Signatures, which should allow identification for them as well . However, as it was noticed, similar variations of Architectures, but with indistinguishable Signatures, were excluded from the Top-16 in advance.Fifth, visual analysis of histograms allows one to assert about the general difference in frequency peaks , which should increase the identification accuracy.Sixth, RiscV64 has a rather interesting signature “portrait”, which is characterized by a larger number of peaks, noticeable in magnitude, from 0.0 to 0.2 (this is clearly seen from the large volume of the painted surface). This can be explained by the specification of the Architecture instructions, which allows uniform use of bytes for encoding for typical programs. Thus, this distinctive feature will play into the hands of the identification of this Class.Seventh, the most weakly expressed Signature is possessed by the Architecture for Ia64, since all of its peaks, except for the 0-th one, do not exceed 0.1 ; it is the success of the identification of this Architecture that will be one of the criteria for the success of the identification.Summing up the preliminary results, we can make a general conclusion that the Signature of MC for a some Architecture differs significantly from the Signature of MC for other CPDoSH Architectures. Consequently, the hypothesis put forward can be considered confirmed.The exclusion of Architectures with similar Signatures is not critical, since they can be combined into one, since they, in fact, represent the same set of processor instructions. At least in the vast majority of cases, static analysis methods and tools will work exactly the same for such variations.Thus, the Hypothesis can be considered completely correct and, therefore, the use of Signatures for assigning of the CPDoSH MC to the appropriate Class is legitimate, which confirms their application for identification.Algorithm 5: Pseudocode for Stage 5 algorithmThe main actions of Stage 1 are presented using Algorithm 5.Algorithm 5 receives a Line 1 starts the algorithm.Line 2 discusses the specifics of In line 3, a Line 5 defines a Line 6 begins a loop to iterate over the OS images , let us compare it with the closest analogs.In , an apprIn , a modelIn , a modelIn  the probIn , the proIn , a methoThe work , similarIn , the proA—Applicability to several Architectures, since CPDoSH can run on a whole set of “non-classic processors” ;B—Possibility of using in methods based on artificial intelligence, since the huge volumes of MC for analysis, the high labor intensity of manual analysis, the lack of the required number of code security specialists, errors in the operation of automatic tools (based on expert rules) require the use of new intelligent technologies;C—Use of unique signatures , since the definition of Architectures from a known set  is required, which can be updated; each Architecture from the set must have a form that is different from the others, which can be interpreted as its unique signature;D—Restoration of meta-information about MC, since the main purpose of the Model is to identify the Architecture of MC, which is itself the required meta-information, and can also be “collected” from auxiliary meta-information;E—Applicability in the tasks of static analysis of MC that go beyond the search for vulnerabilities, since the information security of the CPDoSH is carried out not only by detecting malicious code; for example, it is necessary to check the compatibility of instructions in the MC image with the hardware of the device being used, to take into account the influence of the physical features of the used Architecture (for the MC being executed) on the functioning of the CPDoSH as a whole, to track own vulnerabilities of the Architectures implementation, and so forth.;F—Operability after the destruction of MC, since a feature of cyber-physical devices (including the CPDoSH) is their susceptibility to external physical influences, which also lead to information destruction of MC.Based on the correct research task (identification of the MC CPDoSH Architecture), we introduce the following criteria for comparison with analogues based on the available functionality :The results are presented in The analysis of the comparison results allows us to assert the advantage of the proposed Model (6 points) over analogues . At the same time, the gap from the closest analogue  is 1.5 , as a result of which an electrical breakdown will occur, which will partially destroy the MC in the device. The analysis of such code (both for finding out the causes of the incident and for tasks unrelated to the incident) will require the identification of the Architecture by the “surviving” sections of heterogeneous data.Despite the fact that the total number of more or less popular Architectures , a specific Top-16 was chosen to build Signatures. This is due both to the presence of a specific assembly of the software product for the Architecture data (OS Gentoo), and to the fact that another set of Signatures was obtained by the authors in other researches. However, the proposed scheme of Prototype operation allows obtaining Signatures of practically any Architecture, and all that is required is the selection of a large product and its compilation for all the necessary types of Architectures.In this article, a detailed description of the developed Prototype is omitted; it is also not fully tested. However, this Prototype is only an auxiliary element and is not directly related to the Model of MC. However, the architecture of the extended version of the Prototype, its algorithms and additional modes will be described in the continuation of the study. Extended testing of the Prototype will also be carried out, including checking its performance  under destructive effects on the MC.We also note that the specifics of the CPDoSH area are taken into account in the work by considering a wide range of Architectures applicable for embedded systems ; taking into account the potential significant destruction of MC programs (without the possibility of their restoration by means of the OS); and the importance of investigating crimes involving physical harm to a person due to software vulnerabilities.The conducted investigation was aimed at analyzing CPDoSH MC in the interests of information security.This article is devoted to the CPDoSH MC Model, proposed for use in identifying the Architecture, operating in conditions when any information about this Architecture is absent in principle .The Model has an analytical form and takes into account the features of the structure and execution of programs. The advantage of the Model is its applicability for various tasks of MC research, and not only for architecture identification.The proposed approach is based on the following Hypothesis—the byte-frequency signatures of MC of a given program can be used to unambiguously identify its Architecture.Substantiation of the Hypothesis is identical to the Model adequacy and the Architecture identification efficiency.In the course of the research, the formal Model of the CPDoSH MC was created, containing meta-information about its Architecture. A Hypothesis about the identification of the Architecture was put forward only by the frequency of occurrence of bytes encoding instructions of MC. An experiment with the developed Prototype made it possible to substantiate the validity of this Hypothesis. To construct the Signature, the Top-16 Architectures hypothetically used in the CPDoSH were taken.Thus, a full cycle of research has been carried out: a theoretical model → advanced hypothesis → experimental proof.The total size of all OS images for Top-16 Architectures was 3.2 GB. The total number of bytes for all 16 Architectures files for which Signatures were built  was Further development of the work should be the development of a full-fledged method for identifying the CPDoSH Architecture. This will be followed by the implementation of the method using a software tool, which can be based on the applied research prototype. We will also need to assess the consistency of the Model under various destructive effects on the CPDoSH MC. Interesting from a scientific and practical point of view will be the possibility of using the Model for programs inside CPDoSH that contain the MC of two or more Architectures.


The rats were injected with FGF‐23 or PTH for 5 h before collecting the duodenum, jejunum, and ileum for Mg2+ transport analysis in Ussing chambers. The duodenum, jejunum, and ileum were directly exposed to FGF‐23, PTH, or FGF‐23 plus PTH with or without cell signaling inhibitors for 150 min in Ussing chambers prior to performing the Mg2+ transport study. The small intestinal tissues were also subjected to western blot analyses for FGF receptor (FGFR), PTH receptor (PTHR), Klotho, transient receptor potential melastatin 6 (TRPM6), and cyclin as well as the cystathionine β‐synthase domain divalent metal cation transport mediator 4 (CNNM4) expression. The small intestine abundantly expressed FGFR and PTHR proteins, whereas, Klotho was not expressed in rat small intestine. Systemic PTH or FGF‐23 injection significantly suppressed transcellular Mg2+ transport in the duodenum and jejunum. Direct FGF‐23‐, PTH‐, or FGF‐23 plus PTH exposure also suppressed transcellular Mg2+ absorption in the duodenum and jejunum. There was no additional inhibitory effect of PTH and FGF‐23 on intestinal Mg2+ absorption. The inhibitory effect of PTH, FGF‐23, or FGF‐23 plus PTH was abolished by Gö 6850. Systemic PTH‐ or FGF‐23‐injection significantly decreased membranous TRPM6 expression, but increased cytosolic CNNM4 expression in the duodenum, jejunum, and ileum. In the present study, we propose a novel magnesiotropic action of PTH and FGF‐23 by modulating small intestinal Mg2+ absorption.In the present study, we examined the systemic and direct effects of parathyroid hormone (PTH) and fibroblast growth factor‐23 (FGF‐23) on duodenal, jejunal, and ileal Mg 2+ transport across duodenum, jejunum, and ileum. The direct effect of direct FGF‐23 (c) or PTH (d) exposure on Mg2+ transport across duodenum, jejunum, and ileum.The systemic effect of FGF‐23 injection (a) or PTH injection (b) on Mg In hypocalcemia, high circulating PTH triggers the osteoclast nuclear factor‐κB pathway to stimulate bone resorption, thereby releasing Ca2+ absorption are currently controversial. Chronic PTH injection significantly increased intestinal Mg2+ absorption in rats , and approved by the Ethics Committee on Animal Experiments, Burapha University, Thailand (IACUC 017/2562).Male Sprague‐Dawley rats were obtained from the Nomura Siam International Co. Ltd. . They were acclimatized for 7 days and maintained in open‐top plastic cages at 20 C–25 C and a 12‐h light‐dark cycle. The rats were fed with standard chow  and reverse osmosis water ad libitum. Bodyweight, food intake, and water intake were recorded daily. All experiments were performed following relevant guidelines and regulations, including the ARRIVE guidelines , PTH receptor 1 (PTHR1), α‐Klotho, TRPM6, and CNNM4 .To determine whether systemic PTH and FGF‐23 treatment modulates n Figure , the ratn Figure . The duo2+‐containing or Mg2+‐free supplement 2 h before performing the Mg2+ flux study . The solutions in Ussing chamber setup were maintained at 37 °C and continuously gassed with 5% CO2 in 95% O2. To study the rate of total Mg2+ transport in the first sample of each segment, the apical solution was 40 mmol/l MgCl2‐containing bating solution , meanwhile, the basolateral solution was MgCl2‐free bating solution . To study the rate of paracellular Mg2+ transport, another piece of each intestinal segment was incubated with apical 40 mmol/l MgCl2‐containing bating solution plus potent TRPM6 and Mg2+ channels blocker Co(III)hexamine  with 10% v/v protease inhibitor cocktail (Sigma) before sonicated and centrifuged at 12,000 g for 15 min. Membranous and cytosolic proteins were collected by using Mem‐PER™ Plus Membrane Protein Extraction Kit (Thermo Fisher Scientific Inc.). The membrane was probed with 1:1000 primary antibodies raised against CNNM4 , TRPM6 , PTHR1 , FGFR1 , Klotho , or β‐actin . The membrane was subsequently incubated with 1:5000 HRP‐conjugated secondary antibodies , visualized by Thermo Scientific SuperSignal® West Pico Substrate (Thermo Fisher Scientific Inc.), and captured by the ChemiDoc™ Touch Imaging System . Densitometric analysis was performed using ImageJ for Mac Os X  with Dunnett's posttest was used for the comparison of multiple sets of data. All data were analyzed by GraphPad Prism for Mac Os .Results were expressed as means ± SE. Two sets of data were compared using the unpaired Student's 33.1The expression of FGFR and PTHR was previously shown in the murine small intestine  in the duodenum and jejunum. Whereas transepithelial electrical resistant (TER) was significantly increased in the FGF‐23‐ or PTH 1–34‐exposed duodenum and jejunum. The increment of TER and decrement of Isc was consistent with the suppression of Mg2+ transport in the duodenum and jejunum of FGF‐23‐ or PTH 1–34‐exposed rats. However, the change in PD probably affected transepithelial Mg2+ transport. Therefore, we performed another set of Mg2+ transport experiments. The small intestinal tissues continuously received Isc to nullify PD and to abolish voltage‐dependent Mg2+ transport. As demonstrated in Figure Isc had no effect on the rate of total Mg2+ transport in the control or hormone–exposed duodenum, jejunum, and ileum. These results suggest that small intestinal Mg2+ transport occurred by a trans‐epithelium voltage‐independent mechanism.We also measured transepithelial electrical parameters in the duodenum, jejunum, and ileum after 150 min of Mg3.32+ absorption. The active PTHR antagonist, PTHrR 7–34, also abolished the inhibitory action of active PTH 1–34 on duodenal Mg2+ absorption 2D3 production. The 1,25–1,25(OH)2D3 induces intestinal Ca2+ absorption. The increment of plasma Pi and PTH activates FGF‐23 release which further abolishes 1,25(OH)2D3‐induced intestinal Ca2+ absorption and induces renal Pi excretion. PTH and FGF‐23 synergistically suppressed small intestinal absorption of dietary Mg2+. Therefore, PTH and FGF‐23 exert their harmonious effects in the regulation of plasma Ca2+ levels by preventing hyperphosphatemia and hypermagnesemia.In the present study, we proposed a novel magnesiotropic action for PTH and FGF‐23 by suppressing small intestinal Mg2+ transport. Transepithelial Isc indicates the charge flow across intestinal tissues over time. The decrement in Isc was consistent with low small intestinal Mg2+ absorption in FGF‐23‐ or PTH‐injected rats. We showed that voltage‐dependent transport was not involved in Mg2+ absorption; however, transepithelial ion transport produced PD. If present, the change in PD resulting from the change in Mg2+ transport  may be calculated by the formula: VΔ,Mg2+ = JΔ, Mg2+ × zF × TER . The rate of Mg2+ transport of the control, FGF‐23‐, and PTH‐exposed duodenum was 28.03, 24.79, and 24.03 nmol/hr/cm2, respectively. The TER of FGF‐23‐ and PTH‐exposed duodenum was 183.81 and 163.33 Ω⋅cm2, respectively. Hence, VΔ,Mg2+ in FGF‐23‐ and PTH‐exposed intestine was 31.92 and 35.02 µV, respectively, which were considered negligible.PTH and FGF‐23 increased TER which suppressed paracellular ion transport. However, PTH and FGF‐23 had no effect on paracellular Mgin vivo intestinal Mg2+ absorptions in 0.5 g/kg oral Mg2+‐supplemented rats. In the Ussing chamber setups, systemic and direct exposure to FGF‐23 or PTH suppressed transcellular Mg2+ transport in the duodenum and ileum. The previous study reported that, among high dietary Mg2+ intake groups, the parathyroidectomized (PTX) rat showed significantly higher serum Mg2+ levels compared with that of the sham‐operated rats, whereas the urinary Mg2+ excretion of PTX and sham‐operated rats were comparable 2D3‐induced transient receptor potential cation channel subfamily V (TRPV)‐5, TRPV‐6, and calbindin‐D9k expression, which suppressed duodenal Ca2+ absorption in 1,25(OH)2D3 pre‐treated rats ‐PKC‐dependent pathway by downregulating NaPi‐IIa and NaPi‐IIc expression  293 cells, TRPM6 activity was inhibited by the repressor of estrogen receptor activity (REA) and receptor for activated C‐kinase 1 (RACK1)  of oral Mg2+ supplementation did not affect TRPM6 and CNNM4 expression (data not shown). The exact mechanism regarding the different effects of 0.5 and 1 g/kg oral Mg2+ supplementation on PTH‐ and FGF‐23‐modulated intestinal function could not be explained in the present study; however, the different effects of 0.5 and 1 g/kg Mg2+ supplementation were reported in a previous study  in rats, whereas 1 g/kg oral Mg2+ supplementation markedly enhanced phenytoin action in MES.The high dose (1 g/kg) oral Mg52+‐regulatory action for PTH and FGF‐23 by suppressing small intestinal Mg2+ absorption through a PKC‐dependent mechanism. PTH and FGF‐23 suppressed membranous TRPM6 protein expression, whereas it increased CNNM4 expression in the small intestine of the rats. High dose (1 g/kg) oral Mg2+ supplementation significantly reversed the inhibitory effect of PTH and FGF‐23 by upregulating membranous TRPM6 expression.We proposed a novel MgThe authors declare no competing interests.N.S. designed and performed experiments, analyzed interpreted the results, and wrote the manuscript; N.T. designed and performed experiments, analyzed and interpreted the results, and wrote and edited the manuscript. The authors declare that all data were generated in‐house and that no paper mill was used.


The presence of six major orders of bryozoans with advanced polymorphisms in lower Ordovician rocks strongly suggests a Cambrian origin for the largest and most diverse lophophorate phylum8. However, a lack of convincing bryozoan fossils from the Cambrian period has hampered resolution of the true origins and character assembly of the earliest members of the group. Here we interpret the millimetric, erect, bilaminate, secondarily phosphatized fossil Protomelission gatehousei9 from the early Cambrian of Australia and South China as a potential stem-group bryozoan. The monomorphic zooid capsules, modular construction, organic composition and simple linear budding growth geometry represent a mixture of organic Gymnolaemata and biomineralized Stenolaemata character traits, with phylogenetic analyses identifying P. gatehousei as a stem-group bryozoan. This aligns the origin of phylum Bryozoa with all other skeletonized phyla in Cambrian Age 3, pushing back its first occurrence by approximately 35 million years. It also reconciles the fossil record with molecular clock estimations of an early Cambrian origination and subsequent Ordovician radiation of Bryozoa following the acquisition of a carbonate skeleton13.Bryozoans  are aquatic, dominantly sessile, filter-feeding lophophorates that construct an organic or calcareous modular colonial  exoskeleton Protomelission gatehousei9 as a potential stem-group bryozoan realigns the fossil record with molecular clock estimations of the origins of Bryozoa.Interpretation of the early Cambrian fossil  Nearly all animal phyla, including soft-bodied Deuterostoma14, Entoprocta16, Phoronida17 and Priapulida12, made their first appearance during the Cambrian evolutionary radiation18. A key exception is the ‘missing’ colonial lophotrochozoan phylum Bryozoa, in which six of the eight recognized orders belonging to the classes Stenolaemata and Gymnolaemata appear abruptly with considerable diversity during the early Ordovician period20. Furthermore, there is a major time gap (approximately 44 million years) between the first fossil record of unequivocal bryozoans in the earliest Ordovician (Tremadocian)7 and the deeper origination in the early Cambrian (Terreneuvian) estimated using modern molecular clock analyses21.The Cambrian fossil record chronicles in exceptional detail the emergence of major bilaterian clades and continues to provide chronological constraints on the evolutionary diversification of disparate metazoans from a common ancestor24. The key innovation of modularity initiated a novel pattern of colonial growth that led directly to a burst of morphological diversification and subsequent ecosystem proliferation, especially during the Great Ordovician Biodiversification Event26. Increased fossil sampling has gradually pushed back the oldest occurrence of bryozoans20, most recently into the early Tremadocian4, while the bryozoan affinity of the late Cambrian (Furongian) genus Pywackia remains highly debated18. Hence, a Cambrian origin for Bryozoa is not completely unpredicted and many authors have suggested a non-mineralized organic colony might explain the lack of a Cambrian record for the group20.Bryozoa is the most speciose of the lophophorate phyla firmly nested within Lophotrochozoa, characterized by iterated units (zooids) demonstrating hierarchical levels of modularity, and (apart from one genus) is the only exclusively colonial group of metazoansProtomelission gatehousei9 from the early Cambrian of Australia and South China . The erect colony is composed of two layers of zooids  demonstrate a relatively slow growth of zooids, which probably resulted from high-level control on the relative growth rates across different parts of the colony29  series resulting in a palmate multiserial bilaminate colony Figs. a–g, 4a. 29 Fig. a, 4b, d.29 Fig. . The exh29 Figs. .Fig. 4ReProtomelission gatehousei meets almost all recognition criteria expected in fossil bryozoans2 30. On the basis of phylogenetic analyses, we conclude that P. gatehousei potentially represents a stem-group bryozoan  ancestor25 with skeletal biomineralization independently evolving at least twice across two major bryozoan clades in post-Cambrian times; the Stenolaemata during the Early Ordovician and the Gymnolaemata (Cheilostomata) in the Jurassic period19 , coincident with other major metazoan phyla belonging to the deuterostomes14, lophotrochozoans21 and ecdysozoans25. The miniaturized body-plan, much thinner, unmineralized cuticles (compared to arthropods and ‘worms’) and hard substrate habitat of early bryozoans such as P. gatehousei explain the poor fossil record and cryptic history of bryozoan stem taxa in the Cambrian28. However, the rapid diversification of the Bryozoa30 during the Ordovician probably coincides with calcite seas5, increasing hardground development and more robust biomineralization, leading to increased bryozoan colony size  and enhancing fossilization potential20. Thus, the recognized sequence of appearance for bryozoan taxa over geological time probably does not fully convey the real evolutionary history and may not provide a comprehensive understanding of bryozoan phylogeny7.The discovery of a stem bryozoan in the Cambrian narrows the origination gap that previously existed between the known fossil record and independent molecular clock estimates28 and the phosphatized stem bryozoan reported here reveals a previously hidden history for Bryozoa that provides a new framework for understanding the origin and phylogeny of the phylum7. The honeycomb-like network of zooids in P. gatehousei demonstrates that hierarchical architecture and complexity29 of colonial life was also an important evolutionary innovation during the Cambrian radiation of animal life.The early Cambrian is recognized as an important phosphatization window for microfossil preservation20.We follow the morphological terminology used in previous studies of fossil and extant bryozoans31. Fossils were manually picked from acid residues using a binocular stereo microscope. Five incomplete specimens  were collected from nodular, sandy limestones of the lower Wirrealpa Limestone (Cambrian Stage 4) at the Ten Mile Creek section, Bunkers Graben, South Australia9. One complete specimen of P. gatehousei (Sample ELI XYB 4 AN04) was collected from grey fossiliferous limestones in the Xihaoping Member of the Dengying Formation (Cambrian Stage 3), at the Xiaoyangba section, Hanzhong, South China. The geological and geographic setting has been previously described in detail31.Secondarily phosphatized specimens were recovered through standard acetic leaching of fossiliferous limestone samples, along with abundant benthic filter-feeding brachiopodsIdentified specimens were selected for the study using a Zeiss Supra 35 VP field emission at Uppsala University, Fei Quanta 450-FEGSEM at Northwest University and JEOL JSM 7100F-FESEM at Macquarie University. Coated specimens were further analysed with Backscattered electron imaging (BSE) in Quanta FEG 450 and JEOL JSM 7100F, with attached Energy Dispersive X-ray spectrometry (EDS) system, with 20.0 kV, 60 Pa and WD 11.4 mm at Northwest University and Macquarie University.xy-pixel dimensions of 2.0334 µm (Samples SADME 10470 and ELI XYB 4 AN02). The projections were reconstructed using XMReconstructor Version 7.0.2817 (Carl Zeiss XRM) to produce a series of 16-bit TIFF images with a slice spacing equivalent to the pixel xy dimensions (isotropic voxels) and voxel size of 2.03 µm. The X-ray tomographic microscopy (µCT) images were visualized and segmented via thresholding using ORS Dragonfly 324 version 2020.2 (software available at http://www.theobjects.com/dragonfly). Before feature extraction, images were applied with a normalization filter, unsharp mask and mean shift filter using the image processing function of Dragonfly. Morphological features of interest were coloured separately to assist in distinguishing them from one another. Three-dimensional videos are provided in Supplementary Videos Two specimens were scanned using an Xradia MicroXCT-400 system (Carl Zeiss XRM) with the source operating at 80 kV, 125 µA over 180° sample rotation (−92° to 92°) at The University of Sydney. Geometric and optical magnification settings were chosen to collect projections with P. gatehousei were performed on µCT and SEM images by TpsDig2 v. 2.16. Scatter plots of different specimens, analysed by PAST v. 3, showing morphological variations, were also constructed. Raw data are provided in Supplementary Data Measurements of the length, width and angle of different parts of Protomelission, 18 bryozoan genera and 2 outgroup taxa . The phylogenetic data matrix was built in Microsoft Excel 2016. The 18 bryozoan genera are exemplars of the eight major bryozoan orders, and the fossil genera chosen all occur in the Ordovician . The two outgroup taxa (Eoobolus and Phoronis) correspond to the two major non-bryozoan clades within the Lophophorata. Character codings were based on previously published data . Results of this bootstrap analysis were summarized as a 50% majority rule consensus tree Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-021-04033-w.Supplementary InformationProtomelission gatehousei zooid width provided in Extended Fig. 6a. (2) Raw data of zooid width and length of 13 bryozoan taxa in Extended Fig. 6b. (3) Character trait dataset and morphological states for bryozoans. (4) Character taxon matrix in NEXUS format.This file contains Supplementary Data 1–4. (1) Derived values for box plots of Reporting SummaryPeer Review FileSupplementary Video 1P. gatehousei SADME 10470 (full). This 59-second-3D video reveals the morphology of the external zooid arrangement, along with the transverse, tangential and longitudinal virtual dissection of the internal morphology of the colony of P. gatehousei (Specimen: SADME 10470). False colours are used to differentiate important character traits. Blue, frontal wall; Green, mesotheca with secondary phosphatic cement in yellow; Red, basal wall.3D video of Supplementary Video 2P. gatehousei SADME 10470 . This 13-second-3D video reveals the longitudinal virtual dissection of the internal morphology of the colony of P. gatehousei (Specimen: SADME 10470). False colours are used to differentiate important character traits. Blue, frontal wall; Green, mesotheca with secondary phosphatic cement in yellow; Red, basal wall.3D video of Supplementary Video 3P. gatehousei SADME 10470 . This 6-second-3D video reveals the tangential virtual dissection of the internal morphology of the colony of P. gatehousei (Specimen: SADME 10470). False colours are used to differentiate important character traits. Blue, frontal wall; Green, mesotheca with secondary phosphatic cement in yellow; Red, basal wall.3D video of Supplementary Video 4P. gatehousei SADME 10470 (transverse section). This 24-second-3D video reveals the transverse virtual dissection of the internal morphology of the colony of P. gatehousei (Specimen: SADME 10470). False colours are used to differentiate important character traits. Blue, frontal wall; Green, mesotheca with secondary phosphatic cement in yellow; Red, basal wall.3D video of Supplementary Video 5P. gatehousei SADME 10470. This 25-second-3D video reveals the external zooid arrangement and morphology of the colony of P. gatehousei (Specimen: SADME 10470).3D video of Supplementary Video 6P. gatehousei EIL XYB 4 AN02. This 22-second-3D video reveals the external zooid arrangement and morphology of the colony of P. gatehousei (Specimen: ELI XYB 4 AN02).3D video of