Discriminating Taxonomic Categories and Domains in Mental Simulations of Concepts of Varying Concreteness

Most studies of conceptual knowledge in the brain focus on a narrow range of concrete conceptual categories, rely on the researchers' intuitions about which object belongs to these categories, and assume a broadly taxonomic organization of knowledge. In this fMRI study, we focus on concepts with a variety of concreteness levels; we use a state of the art lexical resource (WordNet 3.1) as the source for a relatively large number of category distinctions and compare a taxonomic style of organization with a domain-based model (associating concepts with scenarios). Participants mentally simulated situations associated with concepts when cued by text stimuli. Using multivariate pattern analysis, we find evidence that all Taxonomic categories and Domains can be distinguished from fMRI data and also observe a clear concreteness effect: Tools and Locations can be reliably predicted for unseen participants, but less concrete categories (e.g., Attributes, Communications, Events, Social Roles) can only be reliably discriminated within participants. A second concreteness effect relates to the interaction of Domain and Taxonomic category membership: Domain (e.g., relation to Law vs. Music) can be better predicted for less concrete categories. We repeated the analysis within anatomical regions, observing discrimination between all/most categories in the left middle occipital and temporal gyri, and more specialized discrimination for concrete categories Tool and Location in the left precentral and fusiform gyri, respectively. Highly concrete/abstract Taxonomic categories and Domain were segregated in frontal regions. We conclude that both Taxonomic and Domain class distinctions are relevant for interpreting neural structuring of concrete and abstract concepts.

The Prevalence of Age-Related Macular Degeneration in Alzheimer's Disease

BACKGROUND:
Age-related macular degeneration (AMD) and Alzheimer's disease (AD) share several features, including the presence of extracellular abnormal deposits associated with neuronal degeneration, drusen, and plaques, respectively. Investigation of any association of AMD and specifically AD is worthwhile but has rarely been done.
OBJECTIVES:
The aim of this study was to determine the prevalence of AMD in subjects with AD in comparison with an age-matched cognitively normal cohort.
METHODS:
Cases were defined as those diagnosed with AD using standardized criteria as part of their clinical care, while controls were cognitively intact individuals aged 65 years or more. Dilated retinal photographs were taken, and a range of potentially confounding factors measured including APOE genotype. AMD features were recorded and AMD grades given.
RESULTS:
Data was collected on 322 controls and 258 cases. While AMD was associated with AD, and the proportion of cases of advanced AMD in AD cases was twice that of controls, when corrected the association was lost. AD was associated with age, the presence of an APOE allele, and smoking, while being 'generally unwell recently' was associated with a reduced risk of AD.
CONCLUSION:
AD and AMD are both associated with age, but our study does not find evidence they are associated with each other. However the retina offers an opportunity to non-invasively image neuronal tissue, and more sophisticated imaging techniques may shed light on ocular biomarkers of AD.

The role of adiposity in cardiometabolic traits:a Mendelian randomization analysis

The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach.

Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture

Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

Diversity and abundance of soil arthropods in urban and suburban holm oak stands

We investigated the soil arthropod communities of urban and suburban holm oak (Quercus ilex L.) stands in a small (Siena) and a large Italian city (Naples) and tested whether the abundance and diversity of higher arthropod taxa are affected by the biotic and abiotic conditions of urban forest soils, including pollution. Acarina and Collembola were the dominant taxa in both cities. In Siena the total number of arthropod individuals collected in the samples was over 1/3 greater than in Naples, but all diversity indices scored higher in Naples than in Siena, probably in response to the higher heterogeneity of microclimatic and pedological conditions found in Naples study area. Oribatids resulted twice more abundant in Siena and so were the total mites with respect to Collembola. While “taxonomic richness” per site increased with distance from road traffic, entropy and evenness indices scored higher at the two ends of the impact gradient in both cities. The overall variation in basic pedological and microbiological soil parameters positively correlated with the total abundance of arthropods, and negatively correlated with their taxonomic richness. At the resolution employed, no significant relation emerged between anthropogenic factors, such as traffic load and soil pollution, and the arthropod fauna density and variety. These results are consistent with conclusions drawn from a previous study on the enchytraeid fauna examined at species level, which is remarkable considering the different taxonomic resolutions of the two studies. CCA results suggest that the higher abundance of Oribatid mites, Protura and Thysanura and the lower abundance of Diplopoda and Symphyla in Siena could depend on a higher fungi/bacteria ratio. This observation can be interpreted in terms of differences in fungi and bacteria between the two cities: Siena is shifted towards the fungal decomposition channel, which supports taxa such as oribatid mites, while Naples is shifted towards the bacterial channel, which supports chiefly detritivorous groups, such as diplopods.

Disturbance, neutral theory and patterns of beta diversity in soil communities

Beta diversity describes how local communities within an area or region differ in species composition/abundance. There have been attempts to use changes in beta diversity as a biotic indicator of disturbance, but lack of theory and methodological caveats have hampered progress. We here propose that the neutral theory of biodiversity plus the definition of beta diversity as the total variance of a community matrix provide a suitable, novel, starting point for ecological applications. Observed levels of beta diversity (BD) can be compared to neutral predictions with three possible outcomes: Observed BD equals neutral prediction or is larger (divergence) or smaller (convergence) than the neutral prediction. Disturbance might lead to either divergence or convergence, depending on type and strength. We here apply these ideas to datasets collected on oribatid mites (a key, very diverse soil taxon) under several regimes of disturbances. When disturbance is expected to increase the heterogeneity of soil spatial properties or the sampling strategy encompassed a range of diverging environmental conditions, we observed diverging assemblages. On the contrary, we observed patterns consistent with neutrality when disturbance could determine homogenization of soil properties in space or the sampling strategy encompassed fairly homogeneous areas. With our method, spatial and temporal changes in beta diversity can be directly and easily monitored to detect significant changes in community dynamics, although the method itself cannot inform on underlying mechanisms. However, human-driven disturbances and the spatial scales at which they operate are usually known. In this case, our approach allows the formulation of testable predictions in terms of expected changes in beta diversity, thereby offering a promising monitoring tool.

Defining the role of common variation in the genomic and biological architecture of adult human height

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated 1/42,000, 1/43,700 and 1/49,500 SNPs explained 1/421%, 1/424% and 1/429% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/I 2-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

Retinal Vascular Caliber, Iris Color, and Age-Related Macular Degeneration in the Irish Nun Eye Study

Pupose. To evaluate the relationship between retinal vascular caliber (RVC), iris color and age-related macular degeneration (AMD) in elderly Irish nuns. Methods. Data from 1233 participants in the cross-sectional observational Irish Nun Eye Study were assessed from digital photographs with a standardized protocol using computer-assisted software. Macular images were graded according to the modified Wisconsin age-related maculopathy grading system. Regression models were used to assess associations, adjusting for age, mean arterial blood pressure, body mass index, refraction and fellow RVC. Results. In total, 1122 (91%) participants had gradable retinal images of sufficient quality for vessel assessment (mean age: 76.3 years [range: 56-100 years]). In an unadjusted analysis, we found some support for a previous finding that individuals with blue iris color had narrower retinal venules compared to those with brown iris color (P<0.05) but this was no longer significant after adjustment. AMD status was categorized as no AMD, any AMD and late AMD only. Individuals with any AMD (early or late AMD) had significantly narrower arterioles and venules compared to those with no AMD in an unadjusted analysis but this was no longer significant after adjustment. A non-significant reduced risk of any AMD or late AMD only was observed in association with brown compared to blue iris color, in both unadjusted and adjusted analyses. Conclusions. RVC was not significantly associated with iris color or early/late AMD after adjustment for confounders. A lower but non-significant AMD risk was observed in those with brown compared to blue iris color.

Challenges in secondary prevention of cardiovascular diseases: A review of the current practice

With the changing demography of populations and increasing prevalence of co-morbidity, frail patients and more complex cardiac conditions, the modern medicine is facing novel challenges leading to rapid innovation where evidence and experiences are lacking. This scenario is also evident in cardiovascular disease prevention, which continuously needs to accommodate its ever changing strategies, settings, and goals. The present paper summarises actual challenges of secondary prevention, and discusses how this intervention should not only be effective but also efficient. By this way the paper tries to bridge the gaps between research and real-world findings and thereby may find ways to improve standard care.

New genetic loci link adipose and insulin biology to body fat distribution

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.

Genetic studies of body mass index yield new insights for obesity biology

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

Using Brain Data for Sentiment Analysis

We present the results of exploratory experiments using lexical valence extracted from brain using electroencephalography (EEG) for sentiment analysis. We selected 78 English words (36 for training and 42 for testing), presented as stimuli to 3 English native speakers. EEG signals were recorded from the subjects while they performed a mental imaging task for each word stimulus. Wavelet decomposition was employed to extract EEG features from the time-frequency domain. The extracted features were used as inputs to a sparse multinomial logistic regression (SMLR) classifier for valence classification, after univariate ANOVA feature selection. After mapping EEG signals to sentiment valences, we exploited the lexical polarity extracted from brain data for the prediction of the valence of 12 sentences taken from the SemEval-2007 shared task, and compared it against existing lexical resources.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.