The Embodied Statistician

How do infants, children, and adults learn grammatical rules from the mere observation of grammatically structured sequences? We present an embodied hypothesis that (a) people covertly imitate stimuli; (b) imitation tunes the particular neuromuscular systems used in the imitation, facilitating transitions between the states corresponding to the successive grammatical stimuli; and (c) the discrimination between grammatical and ungrammatical stimuli is based on differential ease of imitation of the sequences. We report two experiments consistent with the embodied account of statistical learning. Experiment 1 demonstrates that sequences composed of stimuli imitated with different neuromuscular systems were more difficult to learn compared to sequences imitated within a single neuromuscular system. Experiment 2 provides further evidence by showing that selectively interfering with the tuned neuromuscular system while attempting to discriminate between grammatical and ungrammatical sequences disrupted performance only on sequences imitated by that particular neuromuscular system. Together these results are difficult for theories postulating that grammatical rule learning is based primarily on abstract statistics representing transition probabilities

That\u27s My Voice! Participation and Democratic Citizenship in the Early Childhood Classroom

This paper shares a participatory action research study conducted by a team of researchers at a university laboratory school in collaboration with three classroom teachers and 60 preschoolers. The team engaged in this research in order to examine the ways in which school personnel could generate more authentic community service experiences with, rather than simply for, children. Findings illustrate that with the support of adults, children generated ways to address issues, discussed their ideas with adults, reflected on their actions, and understood that their voices were being heard beyond the school community. With this increased participation, young people were able to show and exercise crucial skills and dispositions for democratic citizenship

Manipulations of List Type in the DRM Paradigm: A Review of How Structural and Conceptual Similarity Affect False Memory

The use of list-learning paradigms to explore false memory has revealed several critical findings about the contributions of similarity and relatedness in memory phenomena more broadly. Characterizing the nature of “similarity and relatedness” can inform researchers about factors contributing to memory distortions and about the underlying associative and semantic networks that support veridical memory. Similarity can be defined in terms of semantic properties (e.g., shared conceptual and taxonomic features), lexical/associative properties (e.g., shared connections in associative networks), or structural properties (e.g., shared orthographic or phonological features). By manipulating the type of list and its relationship to a non-studied critical item, we review the effects of these types of similarity on veridical and false memory. All forms of similarity reviewed here result in reliable error rates and the effects on veridical memory are variable. The results across a variety of paradigms and tests provide partial support for a number of theoretical explanations of false memory phenomena, but none of the theories readily account for all results

Shifts in microbial communities in soil, rhizosphere and roots of two major crop systems under elevated CO2 and O3

Rising atmospheric concentrations of CO2 and O3 are key features of global environmental change. To investigate changes in the belowground bacterial community composition in response to elevated CO2 and O3 (eCO2 and eO3) the endosphere, rhizosphere and soil were sampled from soybeans under eCO2 and maize under eO3. The maize rhizosphere and endosphere α-diversity was higher than soybean, which may be due to a high relative abundance of Rhizobiales. Only the rhizosphere microbiome composition of the soybeans changed in response to eCO2, associated with an increased abundance of nitrogen fixing microbes. In maize, the microbiome composition was altered by the genotype and linked to differences in root exudate profiles. The eO3 treatment did not change the microbial communities in the rhizosphere, but altered the soil communities where hybrid maize was grown. In contrast to previous studies that focused exclusively on the soil, this study provides new insights into the effects of plant root exudates on the composition of the belowground microbiome in response to changing atmospheric conditions. Our results demonstrate that plant species and plant genotype were key factors driving the changes in the belowground bacterial community composition in agroecosystems that experience rising levels of atmospheric CO2 and O3

Differences in visceral adipose tissue and biochemical cardiometabolic risk markers in elite rugby union athletes of Caucasian and Polynesian descent

Polynesian individuals are leaner with greater musculature than Caucasians of an equivalent size, and this genetically different morphology provides a physique that is often compatible with success in a number of sports, including rugby union. Evidence indicates that Polynesians have greater stores of absolute and relative abdominal fat mass and this is known to confer cardiometabolic risk. The aims of this study were to (1) explore the relationship between ethnicity, visceral adipose tissue (VAT), and cardiometabolic disease risk markers in elite Caucasian and Polynesian rugby union athletes, and (2) assess the impact of a pre-season training programme on these markers. Twenty-two professional rugby union athletes of Caucasian (n = 11) and Polynesian (n = 11) descent underwent physique assessment via surface anthropometry, dual-energy X-ray absorptiometry, and magnetic resonance imaging before and after an 11-week pre-season. A fasted blood test was undertaken at both time points. Compared to Caucasians, at baseline Polynesians displayed significantly higher VAT (771 ± 609 cm3 vs 424 ± 235 cm3; p = 0.043), triglycerides (1.0 ± 0.9 mmol/L vs 0.6 ± 0.2 mmol/L; p = 0.050), and low-density lipoprotein cholesterol (3.1 ± 0.9 mmol/L vs 2.3 ± 0.7 mmol/L; p = 0.019). Similar changes were observed in both groups over the pre-season period in VAT and blood biochemical markers. Polynesian rugby union athletes were more likely than Caucasians to exhibit risk factors associated with cardiometabolic disease, such as elevated VAT and unfavourable lipid profiles. Further longitudinal research is required to identify and explain the short- and long-term risk of cardiometabolic disease in athletes of Polynesian descent

Deep learning quantification of percent steatosis in donor liver biopsy frozen sections

BACKGROUND: Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies. METHODS: We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model. FINDINGS: The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set). INTERPRETATION: Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation. FUNDING: Mid-America Transplant Society

Bostonia: The Boston University Alumni Magazine. Volume 9

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Biomarkers for Early and Late Stage Chronic Allograft Nephropathy by Proteogenomic Profiling of Peripheral Blood

Despite significant improvements in life expectancy of kidney transplant patients due to advances in surgery and immunosuppression, Chronic Allograft Nephropathy (CAN) remains a daunting problem. A complex network of cellular mechanisms in both graft and peripheral immune compartments complicates the non-invasive diagnosis of CAN, which still requires biopsy histology. This is compounded by non-immunological factors contributing to graft injury. There is a pressing need to identify and validate minimally invasive biomarkers for CAN to serve as early predictors of graft loss and as metrics for managing long-term immunosuppression.We used DNA microarrays, tandem mass spectroscopy proteomics and bioinformatics to identify genomic and proteomic markers of mild and moderate/severe CAN in peripheral blood of two distinct cohorts (n = 77 total) of kidney transplant patients with biopsy-documented histology.Gene expression profiles reveal over 2400 genes for mild CAN, and over 700 for moderate/severe CAN. A consensus analysis reveals 393 (mild) and 63 (moderate/severe) final candidates as CAN markers with predictive accuracy of 80% (mild) and 92% (moderate/severe). Proteomic profiles show over 500 candidates each, for both stages of CAN including 302 proteins unique to mild and 509 unique to moderate/severe CAN.This study identifies several unique signatures of transcript and protein biomarkers with high predictive accuracies for mild and moderate/severe CAN, the most common cause of late allograft failure. These biomarkers are the necessary first step to a proteogenomic classification of CAN based on peripheral blood profiling and will be the targets of a prospective clinical validation study