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Teachers perceive student engagement in many ways, and do not necessarily align engagement strategies they employ with their own conceptualization of this construct. This situation is worsened in online learning of which popularity has grown since the emergence of the COVID-19 global pandemic. We explored perceptions of student engagement in online learning, both synchronous and asynchronous, using semi-structured in-depth interviews with K-12 teachers (N=13). We referred to different dimensions (behavioral, emotional, social, and cognitive) and contexts (student, peers, class, school, and home). Findings suggest that student engagement in online learning has unique characteristics: It is mostly behavioral, particularly task-centered; it relates to student-, class-, and home contexts; and teachers take responsibility on- and act upon issues related to it.

Introduction: Hip fractures in the elderly are related to increased mortality. The identification of patients at risk is essential. Several nutritional and inflammatory parameters were investigated in an effort to find a prognostic indicator for mortality following fragility hip fractures (FHF) surgery. We aim to evaluate their utility and compare between the different factors. Methods: A retrospective cohort study of patients 65 years and older, who underwent surgery following fragility hip fractures between January 2012 and June 2020, was conducted. Patients who died within 90 days were matched at a 1:1 ratio with surviving controls, based on age, gender, fracture type, and comorbidities. Nutritional and inflammatory indices, including serum albumin, protein energy malnutrition (PEM), albumin-to-globulin ratio (AGR), prognostic nutritional index (PNI), the systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), and the neutrophile-to-lymphocyte ratio (NLR), were compared between groups. Results: 304 patients were included, 152 in each group. Patients’ demographics were similar. Among all indices evaluated, only the PLR significantly differed between the study groups (236.9 ± 193.5 for the study group vs. 186.6 ± 119.3 for the control group (p = 0.007). In patients who survived the initial hospitalization, the PEM was also found to be correlated with 90 days mortality. Discussion: The PLR was found to be correlated with mortality risk following FHF surgery. As it can be easily calculated from accessible blood tests, we recommend its’ routine assessment as a screening tool for personalized management of patients at high risk for mortality.

Learning Analytics is concerned with the use of data collected in educational settings to support learning processes. We take a Learning Analytics approach to study the use of immediate feedback in digital classification tasks in mathematics. Feedback serves as an opportunity for learning, however its mere existence does not guarantee its use and effectiveness, as what matters is how learners interact with it. Therefore, our research questions are focused on that interaction. The data consisted of 266 object movements for classifying polygons, and 524 shape movements for classifying traffic signs, under the topic of symmetry. Participants included 29 elementary school students (9-12 years old) from Israel and Germany. Analyzing students' success, feedback use, and the associations between them, we demonstrate how not acting upon feedback is negatively associated with success, and how this undesired behavior slightly reduces along the learning process.

The impact of the COVID-19 pandemic on the higher education sector has been overwhelming, with emergency responses that have affected decision-making processes. Yet, our understanding of higher education instructors' perspectives regarding the process of data-driven decisions, especially in times of emergency, is still limited. We aimed at characterizing the types of data-driven decisions that higher education instructors have made in their courses. This was done while asking the instructors to reflect upon a face-to-face (F2F) course that was suddenly shifted to emergency remote teaching (ERT), due to the COVID-19 pandemic outbreak. Taking a qualitative approach, data were collected via an open-ended online questionnaire distributed among 109 higher education instructors from different countries. The findings suggest that the instructors mentioned a wider range of data sources, and a wider range of data-driven decisions while referring to the ERT mode, compared with their F2F instruction. In F2F teaching, the instructors mostly provided students with real-time educational assistance. In ERT, the instructors mostly adjusted the course requirements, promoted collaboration among students, and offered them social and emotional support.

Synchronous hybrid learning (SHL) is a new instructional approach that allows on-site and remote students to participate in learning activities simultaneously. Investigating metaphorical perceptions about new learning environments may offer insights into the ways in which they are perceived by various stakeholders. Yet, research is missing a thorough investigation into metaphorical perceptions about hybrid learning environments. Hence, our goal was to determine and compare the metaphorical perceptions of higher education instructors and students regarding their roles in face-to-face versus SHL environments. When asked about SHL, participants were asked to refer separately to the on-site and remote student roles. Following the mixed-methods research design, data were obtained from 210 higher education instructors and students who responded to an online questionnaire during the 2021 academic year. Findings showed that both groups perceived their roles differently in face-to-face versus SHL. For instructors, the "guide" metaphor was replaced with the "juggler" and the "counselor" metaphors. For students, the "audience" metaphor was replaced with different metaphors for each cohort of learners. The on-site students were described as an active audience, while the remote students were described as outsiders or observers. The meaning of these metaphors will be discussed in light of the impact of the COVID-19 pandemic on teaching and learning in contemporary higher education.

This article presents a study of a full-day, university-organized, online hackathon that aimed at designing solutions to problems raised in remote learning and teaching. The event, with about 80 participants, was carried out during the COVID-19 pandemic when many schools were closed and shifted to emergency remote teaching. The hackathon participants were challenged to design innovative solutions to authentic problems that have been raised during this situation. Using an online questionnaire, to which 28 of the hackathon participants responded, we quantitatively and qualitatively examined how the event had affected them. Findings suggest that participating in the hackathon mostly contributed to the participants' recognition of the importance of collaboration, and their thinking about academy-society relations. These contributions were driven by the way the hackathon was designed and handled, and the fact that it was held online enabled some of its unique contributions. We also show that online, shorter-than-usual hackathon still has a meaningful, multi-faceted contribution to their participants, and conclude with recommendations for planning such events.

Abstract Four-dimensional scanning transmission electron microscopy, coupled with a wide array of data analytics, has unveiled new insights into complex materials. Here, we introduce a straightforward unsupervised machine learning approach that entails dimensionality reduction and clustering with minimal hyperparameter tuning to semi-automatically identify unique coexisting structures in metallic alloys. Applying cepstral transformation to the original diffraction dataset improves this process by effectively isolating phase information from potential signal ambiguity caused by sample tilt and thickness variations, commonly observed in electron diffraction patterns. In a case study of a NiTiHfAl shape memory alloy, conventional scanning transmission electron microscopy imaging struggles to accurately identify a low-contrast precipitate at lower magnifications, posing challenges for microscale analyses. We find that our method efficiently separates multiple coherent structures while using objective means of determining hyperparameters. Furthermore, we demonstrate how the clustering result facilitates more robust strain mapping to provide immediate and quantitative structural insights.

Abstract Background Recent evidence suggests that certain fetal anomalies detected upon prenatal ultrasound screenings are associated with autism spectrum disorder (ASD). In this cross-sectional study, we aimed to identify genetic variants associated with fetal ultrasound anomalies (UFAs) in children with ASD. Methods The study included all children with ASD who are registered in the database of the Azrieli National Center of Autism and Neurodevelopment and for whom both prenatal ultrasound and whole exome sequencing (WES) data were available. We applied our in-house integrative bioinformatics pipeline, AutScore, to these WES data to prioritize rare, gene-disrupting variants (GDVs) probably contributing to ASD susceptibily. Univariate statistics and multivariable regression were used to assess the associations between UFAs and GDVs identified in these children. Results The study sample comprised 126 children, of whom 43 (34.1%) had at least one UFA detected in the prenatal ultrasound scan. A total of 87 candidate ASD genetic variants were detected in 60 children, with 24 (40%) children carrying multiple variants. Children with UFAs were more likely to have loss-of-function (LoF) mutations (aOR = 2.55, 95%CI: 1.13–5.80). This association was particularly noticeable when children with structural anomalies or children with UFAs in their head and brain scans were compared to children without UFAs (any mutation: aOR = 8.28, 95%CI: 2.29–30.01; LoF: aOR = 5.72, 95%CI: 2.08–15.71 and any mutation: aOR = 6.39, 95%CI: 1.34–30.47; LoF: aOR = 4.50, 95%CI: 1.32–15.35, respectively). GDVs associated with UFAs were enriched in genes highly expressed across all tissues (aOR = 2.76, 95%CI: 1.14–6.68). There was a weak, but significant, correlation between the number of mutations and the number of abnormalities detected in the same children (r = 0.21, P = 0.016). Conclusions The results provide valuable insights into the potential genetic basis of prenatal organogenesis abnormalities associated with ASD and shed light on the complex interplay between genetic factors and fetal development.

Providing educators with understandable, actionable, and trustworthy insights drawn from large-scope heterogeneous learning data is of paramount importance in achieving the full potential of artificial intelligence (AI) in educational settings. Explainable AI (XAI)--contrary to the traditional "black-box" approach--helps fulfilling this important goal. We present a case study of building prediction models for undergraduate students' learning achievement in a Computer Science course, where the development process involves the course instructor as a co-designer, and with the use of XAI technologies to explain the underlying reasoning of several machine learning predictions. The explanations enhance the transparency of the predictions and open the door for educators to share their judgments and insights. It further enables us to refine the predictions by incorporating the educators' contextual knowledge of the course and of the students. Through this human-AI collaboration process, we demonstrate how to achieve a more accountable understanding of students' learning and drive towards transparent and trustworthy student learning achievement prediction by keeping instructors in the loop. Our study highlights that trustworthy AI in education should emphasize not only the interpretability of the predicted outcomes and prediction process, but also the incorporation of subject-matter experts throughout the development of prediction models.

Creativity and computational thinking (CT) have been recognized as crucial skills for the twenty-first century and have been extensively researched in recent years. However, their connections have not yet been fully understood, and there is room to expand the body of knowledge. This study examines the impact of an intervention program to increase creativity on the acquisition of CT. Our findings highlight the contribution of the intervention on learning, particularly the unique contribution to students' acquisition of CT and computational creativity. Educators who want to promote CT should encourage creative thinking, whether through dedicated learning environments or by providing traditional thinking exercises.

Abstract Background & objectives Tumor grade determines prognosis in urothelial carcinoma. The classification of low and high grade is based on nuclear morphological features that include nuclear size, hyperchromasia and pleomorphism. These features are subjectively assessed by the pathologists and are not numerically measured, which leads to high rates of interobserver variability. The purpose of this study is to assess the value of a computer-based image analysis tool for identifying predictors of tumor grade in bladder cancer. Methods Four hundred images of urothelial tumors were graded by five pathologists and two expert genitourinary pathologists using a scale of 1 (lowest grade) to 5 (highest grade). A computer algorithm was used to automatically segment the nuclei and to provide morphometric parameters for each nucleus, which were used to establish the grading algorithm. Grading algorithm was compared to pathologists’ agreement. Results Comparison of the grading scores of the five pathologists with the expert genitourinary pathologists score showed agreement rates between 88.5% and 97.5%.The agreement rate between the two expert genitourinary pathologists was 99.5%. The quantified algorithm based conventional parameters that determine the grade (nuclear size, pleomorphism and hyperchromasia) showed > 85% agreement with the expert genitourinary pathologists. Surprisingly, the parameter that was most associated with tumor grade was the 10th percentile of the nuclear area, and high grade was associated with lower 10th percentile nuclei, caused by the presence of more inflammatory cells in the high-grade tumors. Conclusion Quantitative nuclear features could be applied to determine urothelial carcinoma grade and explore new biologically explainable parameters with better correlation to grade than those currently used.

Sense of success and self-efficacy regarding technology integration in teaching are among the most important factors that influence teachers' well-being and professional development, and may have a substantial impact on student learning. In this quantitative study (N = 735 K-12 teachers in Israel), we explored the factors contributing to sense of success in emergency remote teaching and self-efficacy for integrating technology in teaching following the experience of teaching during COVID-19 days. We use decision-tree models to look at nuanced relations. Overall, our findings highlight the crucial--albeit not surprising--role of experience in teaching with technology as an important factor that promotes sense of success and self-efficacy. Going beyond this factor, we emphasize that emotional difficulties in times of emergency may serve as an important risk factor, and that taking a leading role in school may serve as an important protective factor. We also found an advantage to STEM and Language teachers, compared with Social Sciences and Humanities teachers. Following our findings, we conclude with a set of recommendations that could enhance school-based teaching and learning at large.

In this qualitative study (N=6), we explored insights of first-year students' instructors and advisors into an early identification system aimed at detecting non-thriving students in the context of an all-campus first-year orientation course for undergraduates. Following the development of that prediction model in a bottom-up manner, using a plethora of available data, we focus on how its end-users could help us understand the underlying mechanisms that drive the identification of non-thriving students. As findings suggest, participants were appreciative overall of the prediction and its timing and came up with various behaviours that could explain non-thriving, mostly motivation and engagement. They suggested additional data that could predict non-thriving, including background information, academic engagement, and learning habits.

IntroductionEarly detection of individuals at risk for onset of severe illness is crucial for prevention and early intervention, aiming to mitigate the long-term impact on both the individual and the community. While well-established models exist for predicting the onset and prolonged severity of illness, there is a gap in understanding illness-onset severity. This pilot study aimed to investigate premorbid objective and subjective dimensions of participation in daily life occupations, as well as sensory and cognitive functions as potential markers of the recent-onset mental illness severity.MethodsA total of 50 participants (men: N=26, 52%; women: N=24, 48%), aged 18–40 (M=26.2, SD=5.8) with recent-onset mental illness completed standard, well-established assessments of illness severity, cognitive biases and failures, neurocognitive status, participation in daily life, and sensory responsiveness thorough cross-sectional design. The differences between the groups of the illness severity were explored with descriptive statistics, followed by a Kruskal–Wallis test. Discriminant analysis was used suggesting a multi-varied model for the separation between the groups of illness severity.ResultsThree groups of illness severity exhibited differences in premorbid cognitive functions (F(2)=5.8, p

ABSTRACT Legionella pneumophila grows within membrane-bound vacuoles in phylogenetically diverse hosts. Intracellular growth requires the function of the Icm/Dot type-IVb secretion system, which translocates more than 300 proteins into host cells. A screen was performed to identify L. pneumophila proteins that stimulate mitogen-activated protein kinase (MAPK) activation, using Icm/Dot translocated proteins ectopically expressed in mammalian cells. In parallel, a second screen was performed to identify L. pneumophila proteins expressed in yeast that cause growth inhibition in MAPK pathway-stimulatory high-osmolarity medium. LegA7 was shared in both screens, a protein predicted to be a member of the bacterial cysteine protease family that has five carboxyl-terminal ankyrin repeats. Three conserved residues in the predicted catalytic triad of LegA7 were mutated. These mutations abolished the ability of LegA7 to inhibit yeast growth. To identify other residues important for LegA7 function, a generalizable selection strategy in yeast was devised to isolate mutants that have lost function and no longer cause growth inhibition on a high-osmolarity medium. Mutations were isolated in the two carboxyl-terminal ankyrin repeats, as well as an inter-domain region located between the cysteine protease domain and the ankyrin repeats. These mutations were predicted by AlphaFold modeling to localize to the face opposite from the catalytic site, arguing that they interfere with the positive regulation of the catalytic activity. Based on our data, we present a model in which LegA7 harbors a cysteine protease domain with an inter-domain and two carboxyl-terminal ankyrin repeat regions that modulate the function of the catalytic domain.IMPORTANCELegionella pneumophila grows in a membrane-bound compartment in macrophages during disease. Construction of the compartment requires a dedicated secretion system that translocates virulence proteins into host cells. One of these proteins, LegA7, is shown to activate a stress response pathway in host cells called the mitogen-activated protein kinase (MAPK) pathway. The effects on the mammalian MAPK pathway were reconstructed in yeast, allowing the development of a strategy to identify the role of individual domains of LegA7. A domain similar to cysteine proteases is demonstrated to be critical for impinging on the MAPK pathway, and the catalytic activity of this domain is required for targeting this path. In addition, a conserved series of repeats, called ankyrin repeats, controls this activity. Data are provided that argue the interaction of the ankyrin repeats with unknown targets probably results in activation of the cysteine protease domain.

Background and objectivesPrevious studies suggest that lower mitochondrial DNA (mtDNA) copy number (CN) is associated with neurodegenerative diseases. However, whether mtDNA CN in whole blood is related to endophenotypes of Alzheimer disease (AD) and AD-related dementia (AD/ADRD) needs further investigation. We assessed the association of mtDNA CN with cognitive function and MRI measures in community-based samples of middle-aged to older adults.MethodsWe included dementia-free participants from 9 diverse community-based cohorts with whole-genome sequencing in the Trans-Omics for Precision Medicine (TOPMed) program. Circulating mtDNA CN was estimated as twice the ratio of the average coverage of mtDNA to nuclear DNA. Brain MRI markers included total brain, hippocampal, and white matter hyperintensity volumes. General cognitive function was derived from distinct cognitive domains. We performed cohort-specific association analyses of mtDNA CN with AD/ADRD endophenotypes assessed within ±5 years (i.e., cross-sectional analyses) or 5-20 years after blood draw (i.e., prospective analyses) adjusting for potential confounders. We further explored associations stratified by sex and age (

IntroductionWe studied the replication and generalization of previously identified metabolites potentially associated with global cognitive function in multiple race/ethnicities and assessed the contribution of diet to these associations.MethodsWe tested metabolite-cognitive function associations in U.S.A. Hispanic/Latino adults (n = 2222) from the Community Health Study/ Study of Latinos (HCHS/SOL) and in European (n = 1365) and African (n = 478) Americans from the Atherosclerosis Risk In Communities (ARIC) Study. We applied Mendelian Randomization (MR) analyses to assess causal associations between the metabolites and cognitive function and between Mediterranean diet and cognitive function.ResultsSix metabolites were consistently associated with lower global cognitive function across all studies. Of these, four were sugar-related (e.g., ribitol). MR analyses provided weak evidence for a potential causal effect of ribitol on cognitive function and bi-directional effects of cognitive performance on diet.DiscussionSeveral diet-related metabolites were associated with global cognitive function across studies with different race/ethnicities.HighlightsMetabolites associated with cognitive function in Puerto Rican adults were recently identified. We demonstrate the generalizability of these associations across diverse race/ethnicities. Most identified metabolites are related to sugars. Mendelian Randomization (MR) provides weak evidence for a causal effect of ribitol on cognitive function. Beta-cryptoxanthin and other metabolites highlight the importance of a healthy diet.

This report describes a randomized controlled study that compared the personalization of educational content based on neural networks to personalization by human experts. The study was conducted in a graphically rich online learning environment for elementary school mathematics, in which N = 135 fourth- and sixth-grade students learn via mathematical applets. The performance of students who followed the algorithm's recommendations was compared with that of students who followed an a priori sequence constructed by the experts. While the algorithm only considered students' performance on past problems when recommending new problems, the human experts also took into consideration other factors related both to content and to the graphical interface. The findings reveal no significant differences in performance between the two groups, suggesting that the algorithm was as successful in preparing the students as human teachers. Herein, we discuss the different mechanisms used to prepare each of the groups for the learning tasks and highlight the importance of the user interface in that process. Specifically, we find that applets involving supportive interactions, in which students' interactions were intended to help solve the problem but were optional, represented students' preknowledge, while applets entailing required interactions did not. We contribute to the field of personalization in education with new evidence of the advantages of a content sequencing algorithm-based on collaborative filtering ranking and implemented via a neural network--in a graphically rich environment as tested in authentic classrooms.

IntroductionPolygenic Risk Scores (PRSs) are summaries of genetic risk alleles for an outcome.MethodsWe used summary statistics from five GWASs of AD to construct PRSs in 4,189 diverse Hispanics/Latinos (mean age 63 years) from the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA). We assessed the PRS associations with MCI in the combined set of people and in diverse subgroups, and when including and excluding the APOE gene region. We also assessed PRS associations with MCI in an independent dataset from the Mass General Brigham Biobank.ResultsA simple sum of 5 PRSs ("PRSsum"), each constructed based on a different AD GWAS, was associated with MCI (OR = 1.28, 95% CI [1.14, 1.41]) in a model adjusted for counts of the APOE-[Formula: see text] and APOE-[Formula: see text] alleles. Associations of single-GWAS PRSs were weaker. When removing SNPs from the APOE region from the PRSs, the association of PRSsum with MCI was weaker (OR = 1.17, 95% CI [1.04,1.31] with adjustment for APOE alleles). In all association analyses, APOE-[Formula: see text] and APOE-[Formula: see text] alleles were not associated with MCI.DiscussionA sum of AD PRSs is associated with MCI in Hispanic/Latino older adults. Despite no association of APOE-[Formula: see text] and APOE-[Formula: see text] alleles with MCI, the association of the AD PRS with MCI is stronger when including the APOE region. Thus, APOE variants different than the classic APOE alleles may be important predictors of MCI in Hispanic/Latino adults.

We studied the genetic associations of a previously developed Metabolomic Risk Score (MRS) for Mild Cognitive Impairment (MCI) and beta-aminoisobutyric acid metabolite (BAIBA)-the metabolite highlighted by results from a genome-wide association study (GWAS) of the MCI-MRS, and assessed their association with MCI in datasets of diverse race/ethnicities. We first performed a GWAS for the MCI-MRS and BAIBA, in Hispanic/Latino adults (n = 3890) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We identified ten independent genome-wide significant (p value

BackgroundSleep phenotypes have been reported to be associated with cognitive ageing outcomes. However, there is limited research using genetic variants as proxies for sleep traits to study their associations. We estimated associations between Polygenic Risk Scores (PRSs) for sleep duration, insomnia, daytime sleepiness, and obstructive sleep apnoea (OSA) and measures of cogntive ageing in Hispanic/Latino adults.MethodsWe used summary statistics from published genome-wide association studies to construct PRSs representing the genetic basis of each sleep trait, then we studied the association of the PRSs of the sleep phenotypes with cognitive outcomes in the Hispanic Community Healthy Study/Study of Latinos. The primary model adjusted for age, sex, study centre, and measures of genetic ancestry. Associations are highlighted if their p-value

APOE-ɛ4 risk on Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) differs between race/ethnic groups, presumably due to ancestral genomic background surrounding the APOE locus. We studied whether African and Amerindian ancestry-enriched genetic variants in the APOE region modify the effect of the APOE-ɛ4 alleles on Mild Cognitive Impairment (MCI) in Hispanics/Latinos. We defined African and Amerindian ancestry-enriched variants as those common in one Hispanic/Latino parental ancestry and rare in the other two. We identified such variants in the APOE region with a predicted moderate impact based on the SnpEff tool. We tested their interaction with APOE-ɛ4 on MCI in the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) population and African Americans from the Atherosclerosis Risk In Communities (ARIC) study. We identified 5 Amerindian and 14 African enriched variants with an expected moderate effect. A suggestive significant interaction (p-value = 0.01) was found for one African-enriched variant, rs8112679, located in the ZNF222 gene fourth exon. Our results suggest there are no ancestry-enriched variants with large effect sizes of interaction effects with APOE-ɛ4 on MCI in the APOE region in the Hispanic/Latino population. Further studies are needed in larger datasets to identify potential interactions with smaller effect sizes.

Block programming has been suggested as a way of engaging young learners with the foundations of programming and computational thinking in a syntax-free manner. Indeed, syntax errors--which form one of two broad categories of errors in programming, the other one being logic errors--are omitted while block programming. However, this does not mean that errors are omitted at large in such environments. In this exploratory case study of a learning environment for early programming (Kodetu), we explored errors in block programming of middle school students (N = 123), using log files drawn from a block-based online. Analyzing 1033 failed executions, we found that errors may be driven by either learners' knowledge and behavior, or by the learning environment design. The rate of error types was not associated with the learners' and contextual variables examined, with the exception of task complexity (as defined by SOLO taxonomy). Our findings highlight the importance of learning from errors and of learning environment design.

IntroductionThe most prevalent conceptualization of parenting of our time is intensive parenting which refers to parents’ overinvolvement in children’s lives, placing the child’s needs before others’ needs, including the needs of the parents themselves (i.e., Child-centrism). Intensive parenting is mostly attributed to mothers as they are still bearing the bulk responsibility for child rearing. Nevertheless, as the role of fathers changed in recent decades it is crucial to examine intensive parenting among fathers and understand whether factors that are associated with intensive mothering are associated with intensive fatherhood as well. The current study uses Belsky’s Process of Parenting model to fill-in the gap.MethodsParticipants were 301 Israeli fathers of preschool children aged 22 to 50 years old (M=36.34, SD=5.01). They filled out online self-report questionnaires dealing with intensive parenting style, child temperament, social support, marital satisfaction, and parental self-efficacy.ResultsThe model explained 64% of paternal child-centrism. Fathers who reported having children with more difficult temperament, reported low social support, low marital satisfaction, and low self-efficacy, were more intensive in their parenting style.DiscussionThe present findings are discussed in relation to previous findings regarding maternal child-centrism with an emphasis on their important implications for professionals working with families for the benefit of parents’ and children’s wellbeing.

Abstract Background Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance. Methods The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast. Results The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners. Conclusions The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development – Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.

Background: The Crohn’s Disease Exclusion Diet (CDED) is a whole-foods regimen that has demonstrated efficacy in inducing remission among children and adults with mild-to-moderate disease. While initial studies predominantly originated from Israel, recent years have witnessed the expansion of experiences to diverse cultures, culminating in the recognition of CDED in the latest ESPEN guidelines. However, implementing dietary therapy poses significant challenges across various cultures, necessitating adaptations. Aim and Methods: This case-based study aims to present the collective experience from different cultures, shedding light on the encountered challenges and the corresponding solutions devised to surmount them by convening healthcare providers (dietitians and physicians across six countries and eight cultural settings) with extensive experience in utilizing the CDED. Results and Conclusions: Our findings underscore the efficacy of CDED across diverse cultural contexts and emphasize the pivotal role of dietitians in tailoring the diet to accommodate patients’ cultural behaviors and traditions. We highlight challenges encountered and delineate strategies for overcoming them by customizing the diet and offering tailored guidance. Additionally, we provide insights into implementing CDED in various regions through adjusted recipes and personalized counseling from dietitians. This study contributes to the growing body of literature on CDED, and offers practical guidance for its effective adoption in diverse cultural settings.

Biallelic pathogenic variants in SZT2 result in a neurodevelopmental disorder with shared features, including early-onset epilepsy, developmental delay, macrocephaly, and corpus callosum abnormalities. SZT2 is as a critical scaffolding protein in the amino acid sensing arm of the mTORC1 signalling pathway. Due to its large size (3432 amino acids), lack of crystal structure, and absence of functional domains, it is difficult to determine the pathogenicity of SZT2 missense and in-frame deletions, but these variants are increasingly detected and reported by clinical genetic testing in individuals with epilepsy. To exemplify this latter point, here we describe a cohort of 12 individuals with biallelic SZT2 variants and phenotypic overlap with SZT2-related neurodevelopmental disorders. However, the majority of individuals carried one or more SZT2 variants of uncertain significance (VUS), highlighting the need for functional characterization to determine, which, if any, of these VUS were pathogenic. Thus, we developed a novel individualized platform to identify SZT2 loss-of-function variants in the context of mTORC1 signalling and reclassify VUS. Using this platform, we identified a recurrent in-frame deletion (SZT2 p.Val1984del) which was determined to be a loss-of-function variant and therefore likely pathogenic. Haplotype analysis revealed that this single in-frame deletion is a founder variant in those of Ashkenazi Jewish ancestry. Moreover, this approach allowed us to tentatively reclassify all of the VUS in our cohort of 12 individuals, identifying five individuals with biallelic pathogenic or likely pathogenic variants. Clinical features of these five individuals consisted of early-onset seizures (median 24 months), focal seizures, developmental delay and macrocephaly similar to previous reports. However, we also show a widening of the phenotypic spectrum, as none of the five individuals had corpus callosum abnormalities, in contrast to previous reports. Overall, we present a rapid assay to resolve VUS in SZT2, identify a founder variant in individuals of Ashkenazi Jewish ancestry, and demonstrate that corpus callosum abnormalities is not a hallmark feature of this condition. Our approach is widely applicable to other mTORopathies including the most common causes of the focal genetic epilepsies, DEPDC5, TSC1/2, MTOR and NPRL2/3.

Feedback is a powerful instructional tool. However, even after decades of thorough studies, some questions regarding feedback remain unanswered. In particular, it is yet to be determined whether feedback elaboration is indeed helpful to students. In the study presented here, we take a learning analytics approach to investigate the effect of different types of task-level, computer-based feedback in a popular online learning environment for Mathematics (Khan Academy). Specifically, we run a set of randomized controlled experiments on a large scale--with a total of over 26,000 participants--to study the interplay between feedback type (simple vs. two types of elaboration), the time to re-submit an answer after receiving feedback on incorrect submission to current question (referred to as feedback latency), and success on current and subsequent questions. Results suggest a clear effect of feedback elaboration on feedback latency; superior efficiency of symbolic elaborated feedback over verbal elaborated feedback; and superior efficiency of both over simple feedback. We conclude with implications for practitioners.