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Abstract The cerebellum, through its connectivity with the cerebral cortex, plays an integral role in regulating cognitive and affective processes, and its dysregulation can result in neurodevelopmental disorder (NDD)-related behavioural deficits. Identifying cerebellar-cerebral functional connectivity (FC) profiles in children with NDDs can provide insight into common connectivity profiles and their correlation to NDD-related behaviours. 479 participants from the Province of Ontario Neurodevelopmental Disorders (POND) network (typically developing = 93, Autism Spectrum Disorder = 172, Attention Deficit/Hyperactivity Disorder = 161, Obsessive-Compulsive Disorder = 53, mean age = 12.2) underwent resting-state functional magnetic resonance imaging and behaviour testing (Social Communication Questionnaire, Toronto Obsessive-Compulsive Scale, and Child Behaviour Checklist – Attentional Problems Subscale). FC components maximally correlated to behaviour were identified using canonical correlation analysis. Results were then validated by repeating the investigation in 556 participants from an independent NDD cohort provided from a separate consortium (Healthy Brain Network (HBN)). Replication of canonical components was quantified by correlating the feature vectors between the two cohorts. The two cerebellar-cerebral FC components that replicated to the greatest extent were correlated to, respectively, obsessive-compulsive behaviour (behaviour feature vectors, r POND-HBN = −0.97; FC feature vectors, r POND-HBN = −0.68) and social communication deficit contrasted against attention deficit behaviour (behaviour feature vectors, r POND-HBN = −0.99; FC feature vectors, r POND-HBN = −0.78). The statistically stable (|z| > 1.96) features of the FC feature vectors, measured via bootstrap re-sampling, predominantly comprised of correlations between cerebellar attentional and control network regions and cerebral attentional, default mode, and control network regions. In both cohorts, spectral clustering on FC loading values resulted in subject clusters mixed across diagnostic categories, but no cluster was significantly enriched for any given diagnosis as measured via chi-squared test (p > 0.05). Overall, two behaviour-correlated components of cerebellar-cerebral functional connectivity were observed in two independent cohorts. This suggests the existence of generalizable cerebellar network differences that span across NDD diagnostic boundaries.

Abstract Neurodevelopmental conditions can be associated with decreased health-related quality of life; however, the predictors of these outcomes remain largely unknown. We characterized the predictors of health-related quality of life (HRQoL) in a sample of neurodiverse children and youth. We used a cross-sectional subsample from the Province of Ontario Neurodevelopmental Disorders Network (POND) consisting of those children and young people in the POND dataset with complete study data (total n = 615; 31% female; age: 11.28 years ± 2.84 years). Using a structural equation model, we investigated the effects of demographics (age, sex, socioeconomic status), core features (Social Communication Questionnaire, Toronto Obsessive Compulsive Scale, Strengths and Weaknesses of attention deficit/hyperactivity disorder (ADHD)-symptoms and Normal Behavior), co-occurring symptoms (Child Behaviour Checklist), and adaptive functioning (Adaptive Behaviour Assessment System) on HRQoL (KINDL). A total of 615 participants had complete data for this study (autism = 135, ADHD = 273, subthreshold ADHD = 7, obsessive–compulsive disorder (OCD) = 38, sub-threshold OCD = 1, neurotypical = 161). Of these participants, 190 (31%) identified as female, and 425 (69%) identified as male. The mean age was 11.28 years ± 2.84 years. Health-related quality of life was negatively associated with co-occurring symptoms (B = − 0.6, SE = 0.20, CI (− 0.95, − 0.19), p = 0.004)) and age (B = − 0.1, SE = 0.04, CI (− 0.19, − 0.01), p = 0.037). Fewer co-occurring symptoms were associated with higher socioeconomic status (B = − 0.5, SE = − 0.05, CI (− 0.58, − 0.37), p

Background Parent and child mental health has suffered during the pandemic and transition phase. Structured and shared parenting may be intervention targets beneficial to families who are struggling with parent or child mental health challenges. Aims First, we investigated associations between structured and shared parenting and parent depression symptoms. Second, we investigated associations between structured and shared parenting and depression, hyperactivity/inattention and irritability symptoms in children. Method A total of 1027 parents in two-parent households (4797 observations total; 85.1% mothers) completed online surveys about themselves and their children (aged 2–18 years) from April 2020 to July 2022. Structured parenting and shared parenting responsibilities were assessed from April 2020 to November 2021. Symptoms of parent depression, child depression, child hyperactivity and inattention, child irritability, and child emotional and conduct problems were assessed repeatedly (one to 14 times; median of four times) from April 2020 to July 2022. Results Parents who reported higher levels of shared parenting responsibilities had lower depression symptoms (β = −0.09 to −0.32, all P < 0.01) longitudinally. Parents who reported higher levels of shared parenting responsibilities had children with fewer emotional problems (ages 2–5 years; β = −0.07, P < 0.05), fewer conduct problems (ages 2–5 years; β = −0.09, P < 0.01) and less irritability (ages 13–18 years; β = −0.27, P < 0.001) longitudinally. Structured parenting was associated with fewer conduct problems (ages 2–5 years; β = −0.05, P < 0.05). Conclusions Shared parenting is beneficial for parent and child mental health, even under chaotic or inflexible life conditions. Structured parenting is beneficial for younger children.

Introduction: Poor quality T1-weighted brain scans systematically affect the calculation of brain measures. Removing the influence of such scans requires identifying and excluding scans with noise and artefacts through a quality control (QC) procedure. While QC is critical for brain imaging analyses, it is not yet clear whether different QC approaches lead to the exclusion of the same participants. Further, the removal of poor-quality scans may unintentionally introduce a sampling bias by excluding the subset of participants who are younger and/or feature greater clinical impairment. This study had two aims: (1) examine whether different QC approaches applied to T1-weighted scans would exclude the same participants, and (2) examine how exclusion of poor-quality scans impacts specific demographic, clinical and brain measure characteristics between excluded and included participants in three large pediatric neuroimaging samples. Methods: We used T1-weighted, resting-state fMRI, demographic and clinical data from the Province of Ontario Neurodevelopmental Disorders network (Aim 1: n = 553, Aim 2: n = 465), the Healthy Brain Network (Aim 1: n = 1051, Aim 2: n = 558), and the Philadelphia Neurodevelopmental Cohort (Aim 1: n = 1087; Aim 2: n = 619). Four different QC approaches were applied to T1-weighted MRI (visual QC, metric QC, automated QC, fMRI-derived QC). We used tetrachoric correlation and inter-rater reliability analyses to examine whether different QC approaches excluded the same participants. We examined differences in age, mental health symptoms, everyday/adaptive functioning, IQ and structural MRI-derived brain indices between participants that were included versus excluded following each QC approach. Results: Dataset-specific findings revealed mixed results with respect to overlap of QC exclusion. However, in POND and HBN, we found a moderate level of overlap between visual and automated QC approaches (rtet=0.52–0.59). Implementation of QC excluded younger participants, and tended to exclude those with lower IQ, and lower everyday/adaptive functioning scores across several approaches in a dataset-specific manner. Across nearly all datasets and QC approaches examined, excluded participants had lower estimates of cortical thickness and subcortical volume, but this effect did not differ by QC approach. Conclusion: The results of this study provide insight into the influence of QC decisions on structural pediatric imaging analyses. While different QC approaches exclude different subsets of participants, the variation of influence of different QC approaches on clinical and brain metrics is minimal in large datasets. Overall, implementation of QC tends to exclude participants who are younger, and those who have more cognitive and functional impairment. Given that automated QC is standardized and can reduce between-study differences, the results of this study support the potential to use automated QC for large pediatric neuroimaging datasets.

Metabolic homeostasis requires dynamic catabolic and anabolic processes. Autophagy, an intracellular lysosomal degradative pathway, can rewire cellular metabolism linking catabolic to anabolic processes and thus sustain homeostasis. This is especially relevant in the liver, a key metabolic organ that governs body energy metabolism. Autophagy's role in hepatic energy regulation has just begun to emerge and autophagy seems to have a much broader impact than what has been appreciated in the field. Though classically known for selective or bulk degradation of cellular components or energy-dense macromolecules, emerging evidence indicates autophagy selectively regulates various signaling proteins to directly impact the expression levels of metabolic enzymes or their upstream regulators. Hence, we review three specific mechanisms by which autophagy can regulate metabolism: A) nutrient regeneration, B) quality control of organelles, and C) signaling protein regulation. The plasticity of the autophagic function is unraveling a new therapeutic approach. Thus, we will also discuss the potential translation of promising preclinical data on autophagy modulation into therapeutic strategies that can be used in the clinic to treat common metabolic disorders.

Objective To determine the association between pubertal stage, sex and behavioural profile across and within neurodevelopmental disorders (NDDs) compared with typically developing (TD) youth.Methods This was a cross-sectional study from the Province of Ontario Neurodevelopmental Disorders network, including children/youth with various NDDs and TD controls. Caregivers completed the Child Behavior Checklist (CBCL). Participants were grouped into three puberty stages: prepuberty (Tanner stage 1), early puberty (Tanner stages 2–3) and late puberty (Tanner stages 4–5). The association between pubertal stage and CBCL scores was assessed controlling for sex and diagnosis.Results The analysis included 1043 participants (male=733; 70.3%). A three-way interaction between pubertal status, sex and diagnosis was not significant for internalising or externalising behaviour. Diagnosis was significantly associated with CBCL scores for both internalising (p

BackgroundAtypical processing of unfamiliar, but less so familiar, stimuli has been described in Autism Spectrum Disorder (ASD), in particular in relation to face processing. We examined the construct of familiarity in ASD using familiar and unfamiliar songs, to investigate the link between familiarity and autism symptoms, such as repetitive behavior.MethodsForty-eight children, 24 with ASD (21 males, mean age = 9.96 years ± 1.54) and 24 typically developing (TD) controls (21 males, mean age = 10.17 ± 1.90) completed a music familiarity task using individually identified familiar compared to unfamiliar songs, while magnetoencephalography (MEG) was recorded. Each song was presented for 30 s. We used both amplitude envelope correlation (AEC) and the weighted phase lag index (wPLI) to assess functional connectivity between specific regions of interest (ROI) and non-ROI parcels, as well as at the whole brain level, to understand what is preserved and what is impaired in familiar music listening in this population.ResultsIncreased wPLI synchronization for familiar vs. unfamiliar music was found for typically developing children in the gamma frequency. There were no significant differences within the ASD group for this comparison. During the processing of unfamiliar music, we demonstrated left lateralized increased theta and beta band connectivity in children with ASD compared to controls. An interaction effect found greater alpha band connectivity in the TD group compared to ASD to unfamiliar music only, anchored in the left insula.ConclusionOur results revealed atypical processing of unfamiliar songs in children with ASD, consistent with previous studies in other modalities reporting that processing novelty is a challenge for ASD. Relatively typical processing of familiar stimuli may represent a strength and may be of interest to strength-based intervention planning.

Impairments in emotional face processing are demonstrated by individuals with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which is associated with altered emotion processing networks. Despite accumulating evidence of high rates of diagnostic overlap and shared symptoms between ASD and ADHD, functional connectivity underpinning emotion processing across these two neurodevelopmental disorders, compared to typical developing peers, has rarely been examined. The current study used magnetoencephalography to investigate whole-brain functional connectivity during the presentation of happy and angry faces in 258 children (5–19 years), including ASD, ADHD and typically developing (TD) groups to determine possible differences in emotion processing. Data-driven clustering was also applied to determine whether the patterns of connectivity differed among diagnostic groups. We found reduced functional connectivity in the beta band in ASD compared to TD, and a further reduction in the ADHD group compared to the ASD and the TD groups, across emotions. A group-by-emotion interaction in the gamma frequency band was also observed. Greater connectivity to happy compared to angry faces was found in the ADHD and TD groups, while the opposite pattern was seen in ASD. Data-driven subgrouping identified two distinct subgroups: NDD-dominant and TD-dominant; these subgroups demonstrated emotion- and frequency-specific differences in connectivity. Atypicalities in specific brain networks were strongly correlated with the severity of diagnosis-specific symptoms. Functional connectivity strength in the beta network was negatively correlated with difficulties in attention; in the gamma network, functional connectivity strength to happy faces was positively correlated with adaptive behavioural functioning, but in contrast, negatively correlated to angry faces. Our findings establish atypical frequency- and emotion-specific patterns of functional connectivity between NDD and TD children. Data-driven clustering further highlights a high degree of comorbidity and symptom overlap between the ASD and ADHD children.

Abstract Background A growing body of research has demonstrated associations between specific neurodevelopmental disorders and variation in DNA methylation (DNAm), implicating this molecular mark as a possible contributor to the molecular etiology of these disorders and/or as a novel disease biomarker. Furthermore, genetic risk variants of neurodevelopmental disorders have been found to be enriched at loci associated with DNAm patterns, referred to as methylation quantitative trait loci (mQTLs). Methods We conducted two epigenome-wide association studies in individuals with attention-deficit/hyperactivity disorder (ADHD) or obsessive-compulsive disorder (OCD) (aged 4–18 years) using DNA extracted from saliva. DNAm data generated on the Illumina Human Methylation 450 K array were used to examine the interaction between genetic variation and DNAm patterns associated with these disorders. Results Using linear regression followed by principal component analysis, individuals with the most endorsed symptoms of ADHD or OCD were found to have significantly more distinct DNAm patterns from controls, as compared to all cases. This suggested that the phenotypic heterogeneity of these disorders is reflected in altered DNAm at specific sites. Further investigations of the DNAm sites associated with each disorder revealed that despite little overlap of these DNAm sites across the two disorders, both disorders were significantly enriched for mQTLs within our sample. Conclusions Our DNAm data provide insights into the regulatory changes associated with genetic variation, highlighting their potential utility both in directing GWAS and in elucidating the pathophysiology of neurodevelopmental disorders.

Abstract Background Although there is high co-occurrence between ASD and ADHD, the nature of this co-occurrence remains unclear. Our study aimed to examine the underlying relationship between ASD and ADHD symptoms in a combined sample of children with a primary clinical diagnosis of ASD or ADHD. Methods Participants included children and youth (aged 3-20 years) with a clinical diagnosis of ASD (n = 303) or ADHD (n = 319) for a total of 622 participants. Parents of these children completed the social communication questionnaire (SCQ), a measure of autism symptoms, and the strengths and weaknesses of ADHD and normal behavior (SWAN) questionnaire, a measure of ADHD symptoms. A principal component analysis (PCA) was performed on combined SCQ and SWAN items, followed by a profile analysis comparing normalized component scores between diagnostic groups and gender. Results PCA revealed a four-component solution (inattention, hyperactivity/impulsivity, social-communication, and restricted, repetitive, behaviors, and interests (RRBI)), with no overlap between SCQ and SWAN items in the components. Children with ASD had higher component scores in social-communication and RRBI than children with ADHD, while there was no difference in inattentive and hyperactive/impulsive scores between diagnostic groups. Males had higher scores than females in social-communication, RRBI, and hyperactivity/impulsivity components in each diagnostic group. Limitations We did not formally assess children with ASD for ADHD using our research-criteria for ADHD, and vice versa. High rates of co-occurring ADHD in ASD, for example, may have inflated component scores in inattention and hyperactivity/impulsivity. A disadvantage with using single informant-based reports (i.e., parent-rated questionnaires) is that ASD and ADHD symptoms may be difficult to distinguish by parents, and may be interpreted differently between parents and clinicians. Conclusions ASD and ADHD items loaded on separate components in our sample, suggesting that the measurement structure cannot explain the covariation between the two disorders in clinical samples. High levels of inattention and hyperactivity/impulsivity were seen in both ASD and ADHD in our clinical sample. This supports the need for a dimensional framework that examines neurodevelopmental domains across traditional diagnostic boundaries. Females also had lower component scores across social-communication, RRBI, and hyperactivity/impulsivity than males, suggesting that there may be gender-specific phenotypes related to the two conditions.

The Dilleniaceae is known to produce nectarless flowers pollinated by bees, but the fact that bats ingest Dillenia biflora pollen led us to question pollination assumptions for these trees. We aimed to identify the pollinators of D. biflora, check for nectar presence, and investigate potential for cleistogamy and global prevalence of this pollination system. We examined aspects of the pollination of D. biflora on two Fijian islands using video recordings, direct observations, hand pollination, measurements (flowers, bite marks, nectar), and monitoring. The flowers, receptive for one night, contained copious nectar and had permanently closed globose corollas that required removal by bats for pollination. All the 101 flowers that retained their corolla died and did not produce seeds by cleistogamy. The bat Notopteris macdonaldi was well adapted to corolla removal. Keeping corollas closed until bats manipulate the nectar-rich flowers is a beneficial strategy in high-rainfall environments with many flower parasites. We propose to name a pollination system reliant exclusively on bats "chiropteropisteusis." From clues in the literature, other species in the geographical range of Dillenia are probably chiropteropisunous. Chiropteropisteusis should be investigated in the Old-World range of Dillenia, many species of which are threatened. The remarkable "fall" of the entire corolla observed by an earlier botanist for several species in the genus is most likely attributable to bats. This discovery has important implications for the conservation of bat-dependent trees and their associated fauna, particularly considering the high level of threat faced by flying-foxes globally.

Background: Many phenotypic studies have estimated the degree of comorbidity between Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), but few have examined the latent, or unobserved, structure of combined ASD and ADHD symptoms. This is an important perquisite toward better understanding the overlap between ASD and ADHD.Methods: We conducted a scoping review of studies that examined the factor or latent class structure of ASD and ADHD symptoms within the same clinical or general population sample.Results: Eight studies met final inclusion criteria. Four factor analysis studies found that ASD and ADHD domains loaded separately and one found that some ASD and ADHD domains loaded together. In the three latent class studies, there were evidence of profiles with high levels of co-occurring ASD and ADHD symptoms.Conclusions: Our scoping review provides some evidence of phenotypic overlap between ASD and ADHD at the latent, or unobserved, level, particularly when using a “person-centered” (latent class analysis) vs. a “variable-centered” (factor analysis) approach.

Objective: To summarize existing knowledge about the characteristics of attention problems secondary to traumatic brain injuries (TBI) of all severities in children.Methods: Computerized databases PubMed and PsychINFO and gray literature sources were used to identify relevant studies. Search terms were selected to identify original research examining new ADHD diagnosis or attention problems after TBI in children. Studies were included if they investigated any severity of TBI, assessed attention or ADHD after brain injury, investigated children as a primary or sub-analysis, and controlled for or excluded participants with preinjury ADHD or attention problems.Results: Thirty-nine studies were included in the review. Studies examined the prevalence of and risk factors for new attention problems and ADHD following TBI in children as well as behavioral and neuropsychological factors associated with these attention problems. Studies report a wide range of prevalence rates of new ADHD diagnosis or attention problems after TBI. Evidence indicates that more severe injury, injury in early childhood, or preinjury adaptive functioning problems, increases the risk for new ADHD and attention problems after TBI and both sexes appear to be equally vulnerable. Further, literature suggests that cases of new ADHD often co-occurs with neuropsychiatric impairment in other domains. Identified gaps in our understanding of new attention problems and ADHD include if mild TBI, the most common type of injury, increases risk and what brain abnormalities are associated with the emergence of these problems.Conclusion: This scoping review describes existing studies of new attention problems and ADHD following TBI in children and highlights important risk factors and comorbidities. Important future research directions are identified that will inform the extent of this outcome across TBI severities, its neural basis and points of intervention to minimize its impact.

Previous work from our lab has shown that basal cortisol levels are different between healthy young adults who spontaneously use caudate nucleus-dependent response strategies compared to young adults who use hippocampus-dependent spatial navigation strategies. Young adults who use caudate nucleus dependent strategies display lower basal cortisol levels compared to those who use hippocampus-dependent strategies. In the current study, we assessed navigation strategies in children using a virtual navigation task and measured cortisol at baseline as well as cortisol reactivity to both a psychological and to a physical stressor. Replicating what is observed in adults, we found that children who used caudate nucleus-dependent navigation strategies displayed lower cortisol levels at baseline compared to those who used hippocampus-dependent strategies. The psychological stressor, knowledge that a blood draw would be performed by a nurse, caused a significant increase in cortisol uniquely in response learners. The physical stressor, the actual blood draw, produced a significant increase in cortisol amongst spatial learners that was then comparable to levels observed in response learners. Lower baseline cortisol and higher cortisol psychological stress response observed amongst children who used response strategies may therefore reflect early biological changes during development which may have an impact later in life when considering risk for neuropsychiatric disorders.

Abstract Background To examine the effects of the DPP-4i sitagliptin on CV outcomes during and after incident MI in the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS). Methods TECOS randomized 14,671 participants with type 2 diabetes and atherosclerotic cardiovascular disease (ASCVD) to sitagliptin or placebo, in addition to usual care. For those who had a within-trial MI, we analyzed case fatality, and for those with a nonfatal MI, we examined a composite cardiovascular (CV) outcome (CV death or hospitalization for heart failure [hHF]) by treatment group, using Cox proportional hazards models left-censored at the time of the first within-trial MI, without and with adjustment for potential confounders, in intention-to-treat analyses. Results During TECOS, 616 participants had ≥ 1 MI (sitagliptin group 300, placebo group 316, HR 0.95, 95% CI 0.81–1.11, P = 0.49), of which 25 were fatal [11 and 14, respectively]). Of the 591 patients with a nonfatal MI, 87 (15%) died subsequently, with 66 (11%) being CV deaths, and 57 (10%) experiencing hHF. The composite outcome occurred in 58 (20.1%; 13.9 per 100 person-years) sitagliptin group participants and 50 (16.6%; 11.7 per 100 person-years) placebo group participants (HR 1.21, 95% CI 0.83–1.77, P = 0.32, adjusted HR 1.23, 95% CI 0.83–1.82, P = 0.31). On-treatment sensitivity analyses also showed no significant between-group differences in post-MI outcomes. Conclusions In patients with type 2 diabetes and ASCVD experiencing an MI, sitagliptin did not reduce subsequent risk of CV death or hHF, contrary to expectations derived from preclinical animal models. Trial registration clinicaltrials.gov no. NCT00790205

Abstract Background Children with neurodevelopmental disorders (NDDs) exhibit a shared phenotype that involves executive dysfunctions including impairments in mental flexibility (MF). It is of interest to understand if this phenotype stems from some shared neurobiology. Methods To investigate this possibility, we used magnetoencephalography (MEG) neuroimaging to compare brain activity in children (n = 88; 8–15 years) with autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD), as they completed a set-shifting/mental flexibility task. Results Neuroimaging results revealed a similar parietal activation profile across the NDD, groups suggesting a link to their shared phenotype. Differences in frontal activity differentiated the three clinical groups. Brain-behaviour analyses showed a link with repetitive behaviours suggesting shared dysfunction in the associative loop of the corticostriatal system. Conclusion Our study supports the notion that NDDs may exist along a complex phenotypic/biological continuum. All NDD groups showed a sustained parietal activity profile suggesting that they share a strong reliance on the posterior parietal cortices to complete the mental flexibility task; future studies could elucidate whether this is due to delayed brain development or compensatory functioning. The differences in frontal activity may play a role in differentiating the NDDs. The OCD group showed sustained prefrontal activity that may be reflective of hyperfrontality. The ASD group showed reduced frontal activation suggestive of frontal dysfunction and the ADHD group showed an extensive hypoactivity that included frontal and parietal regions. Brain-behaviour analyses showed a significant correlation with repetitive behaviours which may reflect dysfunction in the associative loop of the corticostriatal system, linked to inflexible behaviours.

Measurements of response inhibition components of reactive inhibition and proactive inhibition within the stop-signal paradigm have been of particular interest to researchers since the 1980s. While frequentist nonparametric and Bayesian parametric methods have been proposed to precisely estimate the entire distribution of reactive inhibition, quantified by stop signal reaction times (SSRT), there is no method yet in the stop signal task literature to precisely estimate the entire distribution of proactive inhibition. We identify the proactive inhibition as the difference of go reaction times for go trials following stop trials versus those following go trials and introduce an Asymmetric Laplace Gaussian (ALG) model to describe its distribution. The proposed method is based on two assumptions of independent trial type (go/stop) reaction times and Ex-Gaussian (ExG) models. Results indicated that the four parametric ALG model uniquely describes the proactive inhibition distribution and its key shape features, and its hazard function is monotonically increasing, as are its three parametric ExG components. In conclusion, the four parametric ALG model can be used for both response inhibition components and its parameters and descriptive and shape statistics can be used to classify both components in a spectrum of clinical conditions.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that often involves impaired cognition, communication difficulties and restrictive, repetitive behaviors. ASD is extremely heterogeneous both clinically and etiologically, which represents one of the greatest challenges in studying the molecular underpinnings of ASD. While hundreds of ASD-associated genes have been identified that confer varying degrees of risk, no single gene variant accounts for >1% of ASD cases. Notably, a large number of ASD-risk genes function as epigenetic regulators, indicating potential epigenetic dysregulation in ASD. As such, we compared genome-wide DNA methylation (DNAm) in the blood of children with ASD (n = 265) to samples from age- and sex-matched, neurotypical controls (n = 122) using the Illumina Infinium HumanMethylation450 arrays.Results: While DNAm patterns did not distinctly separate ASD cases from controls, our analysis identified an epigenetically unique subset of ASD cases (n = 32); these individuals exhibited significant differential methylation from both controls than the remaining ASD cases. The CpG sites at which this subset was differentially methylated mapped to known ASD risk genes that encode proteins of the nervous and immune systems. Moreover, the observed DNAm differences were attributable to altered blood cell composition, i.e., lower granulocyte proportion and granulocyte-to-lymphocyte ratio in the ASD subset, as compared to the remaining ASD cases and controls. This ASD subset did not differ from the rest of the ASD cases in the frequency or type of high-risk genomic variants.Conclusion: Within our ASD cohort, we identified a subset of individuals that exhibit differential methylation from both controls and the remaining ASD group tightly associated with shifts in immune cell type proportions. This is an important feature that should be assessed in all epigenetic studies of blood cells in ASD. This finding also builds on past reports of changes in the immune systems of children with ASD, supporting the potential role of altered immunological mechanisms in the complex pathophysiology of ASD. The discovery of significant molecular and immunological features in subgroups of individuals with ASD may allow clinicians to better stratify patients, facilitating personalized interventions and improved outcomes.

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, which act in different combinations or degrees in different cancers. These variations in mutability phenotypes are predictive of progression outcomes independent of the specific mutations they have produced to date. Here we explore the question of how and to what degree these differences in mutational phenotypes act in a cancer to predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational phenotypes, followed by a machine learning framework to identify key features predictive of progression. Analyses of breast invasive carcinoma and lung carcinoma demonstrate that a large fraction of the risk of future clinical outcomes of cancer progression-overall survival and disease-free survival-can be explained solely from mutational phenotype features derived from the phylogenetic analysis. We further show that mutational phenotypes have additional predictive power even after accounting for traditional clinical and driver gene-centric genomic predictors of progression. These results confirm the importance of mutational phenotypes in contributing to cancer progression risk and suggest strategies for enhancing the predictive power of conventional clinical data or driver-centric biomarkers.

Introduction: There is significant overlap in the type of structural language impairments exhibited by children with autism spectrum disorder (ASD) and children with attention deficit hyperactivity disorder (ADHD). This similarity suggests that the cognitive impairment(s) contributing to the structural language deficits in ASD and ADHD may be shared. Previous studies have speculated that procedural memory deficits may be the shared cognitive impairment. The procedural deficit hypothesis (PDH) argues that language deficits can be explained by differences in the neural structures underlying the procedural memory network. This hypothesis is based on the premise that the neural structures comprising the procedural network support language learning. In this study, we aimed to test the PDH in children with ASD, ADHD, and typical development (TD).Methods: One hundred and sixty-three participants (ages 10–21): 91 with ASD, 26 with ADHD, and 46 with TD, completed standardized measures of cognitive and language ability as well as structural magnetic resonance imaging. We compared the structural language abilities, the neural structures underlying the procedural memory network, and the relationship between structural language and neural structure across diagnostic groups.Results: Our analyses revealed that while the structural language abilities differed across ASD, ADHD, and TD groups, the thickness, area, and volume of the structures supporting the procedural memory network were not significantly different between diagnostic groups. Also, several neural structures were associated with structural language abilities across diagnostic groups. Only two of these structures, the inferior frontal gyrus, and the left superior parietal gyrus, are known to be linked to the procedural memory network.Conclusions: The inferior frontal gyrus and the left superior parietal gyrus, have well-established roles in language learning independent of their role as part of the procedural memory system. Other structures such as the caudate and cerebellum, with critical roles in the procedural memory network, were not associated with structural language abilities across diagnostic groups. It is unclear whether the procedural memory network plays a fundamental role in language learning in ASD, ADHD, and TD.

As continued COVID-19 disruption looks likely across the world, perhaps until 2021, contingency plans are evolving in case of further disruption in the 2020–2021 academic year. This includes delivering face-to-face programs fully online for at least part of the upcoming academic year for new and continuing cohorts. This temporary pivot will necessitate distance teaching and learning across almost every conceivable pedagogy, from fundamental degrees to professionally accredited ones. Each institution, program, and course will have its own myriad of individualized needs; however, there is a common question that unites us all: how do we provide teaching and assessment to students in a manner that is accessible, fair, equitable, and provides the best learning whilst acknowledging the temporary nature of the pivot? No “one size fits all” solution exists, and many of the choices that need to be made will be far from simple; however, this paper provides a starting point and basic principles to facilitate discussions taking place around the globe by balancing what we know from the pedagogy of online learning with the practicalities imposed by this crisis and any future crises.

Demographics of the science, technology, engineering, and mathematics (STEM) workforce and student body in the US and Europe continue to show severe underrepresentation of Black, Indigenous, and people of color (BIPOC). Among the documented causes of the persistent lack of diversity in STEM are bias, discrimination, and harassment of members of underrepresented minority groups (URMs). These issues persist due to continued marginalization, power imbalances, and lack of adequate policies against misconduct in academic and other scientific institutions. All scientists can play important roles in reversing this trend by shifting the culture of academic workplaces to intentionally implement equitable and inclusive policies, set norms for acceptable workplace conduct, and provide opportunities for mentorship and networking. As scientists are increasingly acknowledging the lack of racial and ethnic diversity in science, there is a need for clear direction on how to take antiracist action. Here we present 10 rules to help labs develop antiracists policies and action in an effort to promote racial and ethnic diversity, equity, and inclusion in science.

Contributing to broader impacts is an important aspect of scientific research. Engaging practicing K–12 teachers as part of a research project can be an effective approach for addressing broader impacts requirements of grants, while also advancing researcher and teacher professional growth. Our focus is on leveraging teachers’ professional expertise to develop science education materials grounded in emerging scientific research. In this paper, we describe ten simple rules for planning, implementing, and evaluating teacher engagement to support the broader impact goals of your research project. These collaborations can lead to the development of instructional materials or activities for students in the classroom or provide science research opportunities for teachers. We share our successes and lessons learned while collaborating with high school biology teachers to create technology-based, instructional materials developed from basic biological research. The rules we describe are applicable across teacher partnerships at any grade level in that they emphasize eliciting and respecting teachers’ professionalism and expertise.

Metastasis is the primary mechanism by which cancer results in mortality and there are currently no reliable treatment options once it occurs, making the metastatic process a critical target for new diagnostics and therapeutics. Treating metastasis before it appears is challenging, however, in part because metastases may be quite distinct genomically from the primary tumors from which they presumably emerged. Phylogenetic studies of cancer development have suggested that changes in tumor genomics over stages of progression often result from shifts in the abundance of clonal cellular populations, as late stages of progression may derive from or select for clonal populations rare in the primary tumor. The present study develops computational methods to infer clonal heterogeneity and dynamics across progression stages via deconvolution and clonal phylogeny reconstruction of pathway-level expression signatures in order to reconstruct how these processes might influence average changes in genomic signatures over progression. We show, via application to a study of gene expression in a collection of matched breast primary tumor and metastatic samples, that the method can infer coarse-grained substructure and stromal infiltration across the metastatic transition. The results suggest that genomic changes observed in metastasis, such as gain of the ErbB signaling pathway, are likely caused by early events in clonal evolution followed by expansion of minor clonal populations in metastasis, a finding that may have translational implications for early detection or prevention of metastasis1.

The distribution of single Stop Signal Reaction Times (SSRT) in the stop signal task (SST) has been modelled with two general methods: a nonparametric method by Hans Colonius (1990) and a Bayesian parametric method by Dora Matzke, Gordon Logan and colleagues (2013). These methods assume an equal impact of the preceding trial type (go/stop) in the SST trials on the SSRT distributional estimation without addressing the relaxed assumption. This study presents the required model by considering a two-state mixture model for the SSRT distribution. It then compares the Bayesian parametric single SSRT and mixture SSRT distributions in the usual stochastic order at the individual and the population level under ex-Gaussian (ExG) distributional format. It shows that compared to a single SSRT distribution, the mixture SSRT distribution is more varied, more positively skewed, more leptokurtic and larger in stochastic order. The size of the results’ disparities also depends on the choice of weights in the mixture SSRT distribution. This study confirms that mixture SSRT indices as a constant or distribution are significantly larger than their single SSRT counterparts in the related order. This result offers a vital improvement in the SSRT estimations.

Abstract Background Children with traumatic brain injury (TBI) are frequently at risk of long-term impairments of attention and executive functioning but these problems are difficult to predict. Although deficits have been reported to vary with injury severity, age at injury and sex, prognostication of outcome remains imperfect at a patient-specific level. The objective of this proof of principle study was to evaluate a variety of patient variables, along with six brain-specific and inflammatory serum protein biomarkers, as predictors of long-term cognitive outcome following paediatric TBI. Method Outcome was assessed in 23 patients via parent-rated questionnaires related to attention deficit hyperactivity disorder (ADHD) and executive functioning, using the Conners 3rd Edition Rating Scales (Conners-3) and Behaviour Rating Inventory of Executive Function (BRIEF) at a mean time since injury of 3.1 years. Partial least squares (PLS) analyses were performed to identify factors measured at the time of injury that were most closely associated with outcome on (1) the Conners-3 and (2) the Behavioural Regulation Index (BRI) and (3) Metacognition Index (MI) of the BRIEF. Results Higher levels of neuron specific enolase (NSE) and lower levels of soluble neuron cell adhesion molecule (sNCAM) were associated with higher scores on the inattention, hyperactivity/impulsivity and executive functioning scales of the Conners-3, as well as working memory and initiate scales of the MI from the BRIEF. Higher levels of NSE only were associated with higher scores on the inhibit scale of the BRI. Conclusions NSE and sNCAM show promise as reliable, early predictors of long-term attention-related and executive functioning problems following paediatric TBI.

Background: Social-communication difficulties, a hallmark of ASD, autism spectrum disorder (ASD) are often observed in attention – deficit/ hyperactivity disorder (ADHD), although are not part of its diagnostic criteria. Despite sex differences in the prevalence of ASD and ADHD, research examining how sex differences manifest in social and communication functions in these disorders remains limited, and findings are mixed. This study investigated potential sex differences with age in social adaptive function across these disorders, relative to controls.Method: One hundred fifteen youth with ASD, 172 youth with ADHD, and 63 typically developing controls (age range 7–13 years, 75% males) were recruited from the Province of Ontario Neurodevelopmental Disorder (POND) Network. Social adaptive function was assessed using the Adaptive Behavior Assessment System-Second Edition (ABAS-II). The proportions of adaptive behaviors present in each skill area were analyzed as a binomial outcome using logistic regression, controlling for age, and testing for an age-by-sex interaction. In an exploratory analysis, we examined the impact of controlling for core symptom severity on the sex effect.Results: Significant sex-by-age interactions were seen within ASD in the communication (p = 0.005), leisure (p = 0.003), and social skill areas (p < 0.0001). In all three areas, lower scores (indicating poorer function) were found in females compared to males at older ages despite females performing better at younger ages. There were significant differences in the sex-by-age interactions in the social and leisure domains between those with ASD and typically developing controls, with typically developing females showing better scores at older, compared to younger, ages. There were also significant differences in the sex-by-age interactions between ASD and ADHD on the social and leisure domains, as females with ADHD consistently scored higher on social skills than males across all ages, unlike those with ASD. Sex differences across age in the social domains for ADHD were similar to those in the typically developing group.Conclusion: Sex differences in social and communication skill areas were observed between ASD and ADHD, and typically developing controls, with females with ASD performing worse than males at older ages, despite an earlier advantage. These findings reinforce the need to take a developmental approach to understanding sex differences which may have diagnostic, prognostic, and treatment implications.

Involving the public in scientific discovery offers opportunities for engagement, learning, participation, and action. Since its launch in 2007, the CitSci.org platform has supported hundreds of community-driven citizen science projects involving thousands of participants who have generated close to a million scientific measurements around the world. Members using CitSci.org follow their curiosities and concerns to develop, lead, or simply participate in research projects. While professional scientists are trained to make ethical determinations related to the collection of, access to, and use of information, citizen scientists and practitioners may be less aware of such issues and more likely to become involved in ethical dilemmas. In this era of big and open data, where data sharing is encouraged and open science is promoted, privacy and openness considerations can often be overlooked. Platforms that support the collection, use, and sharing of data and personal information need to consider their responsibility to protect the rights to and ownership of data, the provision of protection options for data and members, and at the same time provide options for openness. This requires critically considering both intended and unintended consequences of the use of platforms, data, and volunteer information. Here, we use our journey developing CitSci.org to argue that incorporating customization into platforms through flexible design options for project managers shifts the decision-making from top-down to bottom-up and allows project design to be more responsive to goals. To protect both people and data, we developed—and continue to improve—options that support various levels of “open” and “closed” access permissions for data and membership participation. These options support diverse governance styles that are responsive to data uses, traditional and indigenous knowledge sensitivities, intellectual property rights, personally identifiable information concerns, volunteer preferences, and sensitive data protections. We present a typology for citizen science openness choices, their ethical considerations, and strategies that we are actively putting into practice to expand privacy options and governance models based on the unique needs of individual projects using our platform.

ADHD is associated with altered dopamine regulated reinforcement learning on prediction errors. Despite evidence of categorically altered error processing in ADHD, neuroimaging advances have largely investigated models of normal reinforcement learning in greater detail. Further, although reinforcement leaning critically relies on ventral striatum exerting error magnitude related thresholding influences on substantia nigra (SN) and dorsal striatum, these thresholding influences have never been identified with neuroimaging. To identify such thresholding influences, we propose that error magnitude related activities must first be separated from opposite activities in overlapping neural regions during error detection. Here we separate error detection from magnitude related adjustment (post-error slowing) during inhibition errors in the stop signal task in typically developing (TD) and ADHD adolescents using fMRI. In TD, we predicted that: 1) deactivation of dorsal striatum on error detection interrupts ongoing processing, and should be proportional to right frontoparietal response phase activity that has been observed in the SST; 2) deactivation of ventral striatum on post-error slowing exerts thresholding influences on, and should be proportional to activity in dorsal striatum. In ADHD, we predicted that ventral striatum would instead correlate with heightened amygdala responses to errors. We found deactivation of dorsal striatum on error detection correlated with response-phase activity in both groups. In TD, post-error slowing deactivation of ventral striatum correlated with activation of dorsal striatum. In ADHD, ventral striatum correlated with heightened amygdala activity. Further, heightened activities in locus coeruleus (norepinephrine), raphe nucleus (serotonin) and medial septal nuclei (acetylcholine), which all compete for control of DA, and are altered in ADHD, exhibited altered correlations with SN. All correlations in TD were replicated in healthy adults. Results in TD are consistent with dopamine regulated reinforcement learning on post-error slowing. In ADHD, results are consistent with heightened activities in the amygdala and non-dopaminergic neurotransmitter nuclei preventing reinforcement learning.

The Stop Signal Reaction Time (SSRT) is a latency measurement for the unobservable human brain stopping process, and was formulated by Logan (1994) without consideration of the nature (go/stop) of trials that precede the stop trials. Two asymptotically equivalent and larger indices of mixture SSRT and weighted SSRT were proposed in 2017 to address this issue from time in task longitudinal perspective, but estimation based on the time series perspective has still been missing in the literature. A time series-based state space estimation of SSRT was presented and it was compared with Logan 1994 SSRT over two samples of real Stop Signal Task (SST) data and the simulated SST data. The results showed that time series-based SSRT is significantly larger than Logan’s 1994 SSRT consistent with former Longitudinal-based findings. As a conclusion, SSRT indices considering the after effects of inhibition in their estimation process are larger yielding to hypothesize a larger estimates of SSRT using information on the reactive inhibition, proactive inhibition and their interplay in the SST data.

This study, conducted by the Dr. Edgar Mitchell Foundation for Research into Extraterrestrial and Extraordinary Experiences (FREE), represents the first comprehensive investigation on individuals (N = 3,256) who have reported to have had various forms of contact experience (CE) with a non-human intelligent being (NHI) associated with or without an unidentified aerial phenomenon (UAP). Our research methodology utilized two comprehensive quantitative surveys totaling 554 questions administered in subjects with reported non-hypnotic memory recall of their CE. This survey addressed a diverse range of physical, psychological, perceptual, and paranormal aspects of reported non-hypnotic based recall of both physical and/or non-physical interactions with a NHI being. The results revealed complex reported CEs that involve both physical and non-physical (psychological outcomes, non-ordinary states of consciousness, and paranormal experiences) events. What may be the most significant aspect of the interim results is that approximately 70% (N=2,279) of the study population claimed that their CE changed their life in a “positive way.” In contrast, only 15-20% reported a “negative” impact from their CE. Further, the majority of subjects did not report events typically associated with the traditional held beliefs regarding the “alien-abduction” phenomena. That is, the results suggest that the reported CE with NHI is largely non-physical and can occur via telepathy, during an out-of body experience, being floated into a "matrix-like" reality, as well as through physical interaction on board a craft. Consequently, the results suggest that a non-physical (“contactee”) CE is distinctly different than the physical (“abduction”) CE and should be studied as separate but interrelated anomalous events. In fact, the CE associated with a UAP is not the predominant form of CE and that sighting a UAP is not necessarily associated with a CE. Consequently, future studies should not focus exclusively on the analysis of UAP sightings and traces alone which, based on decades of research, have not advanced our understanding of the possible force which governs and regulates this complex phenomenon. This is an important consideration since the FREE study dispels the notion that contact with NHI beings must always entail either a physical abduction or a landed craft with beings interacting with humans. This study may serve as a needed foundation for researchers to build upon for validation purposes to better understand a unique and diverse range of reported physical and non-physical type CEs with a NHI associated with or without a UAP.

Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML approaches is radiogenomics, which is the study of the impact of genomic variations on the sensitivity of normal and tumor tissue to radiation. Currently, patients undergoing radiotherapy are treated using uniform dose constraints specific to the tumor and surrounding normal tissues. This is suboptimal in many ways. First, the dose that can be delivered to the target volume may be insufficient for control but is constrained by the surrounding normal tissue, as dose escalation can lead to significant morbidity and rare. Second, two patients with nearly identical dose distributions can have substantially different acute and late toxicities, resulting in lengthy treatment breaks and suboptimal control, or chronic morbidities leading to poor quality of life. Despite significant advances in radiogenomics, the magnitude of the genetic contribution to radiation response far exceeds our current understanding of individual risk variants. In the field of genomics, ML methods are being used to extract harder-to-detect knowledge, but these methods have yet to fully penetrate radiogenomics. Hence, the goal of this publication is to provide an overview of ML as it applies to radiogenomics. We begin with a brief history of radiogenomics and its relationship to precision medicine. We then introduce ML and compare it to statistical hypothesis testing to reflect on shared lessons and to avoid common pitfalls. Current ML approaches to genome-wide association studies are examined. The application of ML specifically to radiogenomics is next presented. We end with important lessons for the proper integration of ML into radiogenomics.

Bioinformatics is recognized as part of the essential knowledge base of numerous career paths in biomedical research and healthcare. However, there is little agreement in the field over what that knowledge entails or how best to provide it. These disagreements are compounded by the wide range of populations in need of bioinformatics training, with divergent prior backgrounds and intended application areas. The Curriculum Task Force of the International Society of Computational Biology (ISCB) Education Committee has sought to provide a framework for training needs and curricula in terms of a set of bioinformatics core competencies that cut across many user personas and training programs. The initial competencies developed based on surveys of employers and training programs have since been refined through a multiyear process of community engagement. This report describes the current status of the competencies and presents a series of use cases illustrating how they are being applied in diverse training contexts. These use cases are intended to demonstrate how others can make use of the competencies and engage in the process of their continuing refinement and application. The report concludes with a consideration of remaining challenges and future plans.

With increasing appreciation for the extent and importance of intratumor heterogeneity, much attention in cancer research has focused on profiling heterogeneity on a single patient level. Although true single-cell genomic technologies are rapidly improving, they remain too noisy and costly at present for population-level studies. Bulk sequencing remains the standard for population-scale tumor genomics, creating a need for computational tools to separate contributions of multiple tumor clones and assorted stromal and infiltrating cell populations to pooled genomic data. All such methods are limited to coarse approximations of only a few cell subpopulations, however. In prior work, we demonstrated the feasibility of improving cell type deconvolution by taking advantage of substructure in genomic mixtures via a strategy called simplicial complex unmixing. We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures, with specific emphasis on genome-wide copy number variation (CNV) data, as well as the ability to process quantitative RNA expression data, and heterogeneous combinations of RNA and CNV data. We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse, noisy data; and automated model inference methods for other key model parameters. We further demonstrate their effectiveness in identifying mixture substructure in true breast cancer CNV data from the Cancer Genome Atlas (TCGA). Source code is available at https://github.com/tedroman/WSCUnmix.

Advances in fluorescence in situ hybridization (FISH) make it feasible to detect multiple copy-number changes in hundreds of cells of solid tumors. Studies using FISH, sequencing, and other technologies have revealed substantial intra-tumor heterogeneity. The evolution of subclones in tumors may be modeled by phylogenies. Tumors often harbor aneuploid or polyploid cell populations. Using a FISH probe to estimate changes in ploidy can guide the creation of trees that model changes in ploidy and individual gene copy-number variations. We present FISHtrees 3.0, which implements a ploidy-based tree building method based on mixed integer linear programming (MILP). The ploidy-based modeling in FISHtrees includes a new formulation of the problem of merging trees for changes of a single gene into trees modeling changes in multiple genes and the ploidy. When multiple samples are collected from each patient, varying over time or tumor regions, it is useful to evaluate similarities in tumor progression among the samples. Therefore, we further implemented in FISHtrees 3.0 a new method to build consensus graphs for multiple samples. We validate FISHtrees 3.0 on a simulated data and on FISH data from paired cases of cervical primary and metastatic tumors and on paired breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Tests on simulated data show improved accuracy of the ploidy-based approach relative to prior ploidyless methods. Tests on real data further demonstrate novel insights these methods offer into tumor progression processes. Trees for DCIS samples are significantly less complex than trees for paired IDC samples. Consensus graphs show substantial divergence among most paired samples from both sets. Low consensus between DCIS and IDC trees may help explain the difficulty in finding biomarkers that predict which DCIS cases are at most risk to progress to IDC. The FISHtrees software is available at ftp://ftp.ncbi.nih.gov/pub/FISHtrees.

The environment of a living cell is vastly different from that of an in vitro reaction system, an issue that presents great challenges to the use of in vitro models, or computer simulations based on them, for understanding biochemistry in vivo. Virus capsids make an excellent model system for such questions because they typically have few distinct components, making them amenable to in vitro and modeling studies, yet their assembly can involve complex networks of possible reactions that cannot be resolved in detail by any current experimental technology. We previously fit kinetic simulation parameters to bulk in vitro assembly data to yield a close match between simulated and real data, and then used the simulations to study features of assembly that cannot be monitored experimentally. The present work seeks to project how assembly in these simulations fit to in vitro data would be altered by computationally adding features of the cellular environment to the system, specifically the presence of nucleic acid about which many capsids assemble. The major challenge of such work is computational: simulating fine-scale assembly pathways on the scale and in the parameter domains of real viruses is far too computationally costly to allow for explicit models of nucleic acid interaction. We bypass that limitation by applying analytical models of nucleic acid effects to adjust kinetic rate parameters learned from in vitro data to see how these adjustments, singly or in combination, might affect fine-scale assembly progress. The resulting simulations exhibit surprising behavioral complexity, with distinct effects often acting synergistically to drive efficient assembly and alter pathways relative to the in vitro model. The work demonstrates how computer simulations can help us understand how assembly might differ between the in vitro and in vivo environments and what features of the cellular environment account for these differences.

Anaplastic lymphoma kinase protein (ALK)-negative anaplastic large cell lymphoma (ALCL) has a vast morphologic spectrum and may mimic many other types of malignancies both cytologically and histologically. There are only a few published case reports/series describing the cytomorphologic features of ALCL on fine-needle aspiration (FNA) biopsy specimens. We describe a case of ALK-negative ALCL mimicking a high-grade soft tissue sarcoma of the thigh in a 62-year-old man. The characteristic morphologic findings on FNA and core biopsy along with the immunophenotypic profile are described and reviewed. The diagnosis of ALCL on FNA biopsy may be difficult, but can be done successfully with the use of ancillary tests. Therefore, it must be considered in the differential diagnosis of lesions with pleomorphism, anaplasia, and wreath-like or horseshoe-shaped nuclei to ensure that adequate material is obtained for ancillary studies.

Citizen science projects have the potential to advance science by increasing the volume and variety of data, as well as innovation. Yet this potential has not been fully realized, in part because citizen science data are typically not widely shared and reused. To address this and related challenges, we built CitSci.org (see www.citsci.org), a customizable platform that allows users to collect and generate diverse datasets. We hope that CitSci.org will ultimately increase discoverability and confidence in citizen science observations, encouraging scientists to use such data in their own scientific research.

Often we must balance being prepared to act quickly with being prepared to suddenly stop. The stop signal task (SST) is widely used to study inhibitory control, and provides a measure of the speed of the stop process that is robust to changes in subjects' response strategy. Previous studies have shown that preparation affects inhibition. We used fMRI to separate activity that occurs after a brief (500 ms) warning stimulus (warning-phase) from activity that occurs during responses that follow (response-phase). Both of these phases could contribute to the preparedness to stop because they both precede stop signals. Warning stimuli activated posterior networks that signal the need for top-down control, whereas response phases engaged prefrontal and subcortical networks that implement top-down control. Regression analyses revealed that both of these phases affect inhibitory control in different ways. Warning-phase activity in the cerebellum and posterior cingulate predicted stop latency and accuracy, respectively. By contrast, response-phase activity in fronto-temporal areas and left striatum predicted go speed and stop accuracy, in pre-supplementary motor area affected stop accuracy, and in right striatum predicted stop latency and accuracy. The ability to separate hidden contributions to inhibitory control during warning-phases from those during response-phases can aid in the study of models of preparation and inhibitory control, and of disorders marked by poor top-down control.