Prevalence of face recognition deficits in middle childhood

Approximately 2-2.5% of the adult population is believed to show severe difficulties with face recognition, in the absence of any neurological injury – a condition known as developmental prosopagnosia (DP). However, to date no research has attempted to estimate the prevalence of face recognition deficits in children, possibly because there are very few child-friendly, well-validated tests of face recognition. In the current study, we examined face and object recognition in a group of primary school children (aged 5-11 years), to establish whether our tests were suitable for children; and to provide an estimate of face recognition difficulties in children. In Experiment 1 (n = 184), children completed a pre-existing test of child face memory, the CFMT-K, and a bicycle test with the same format. In Experiment 2 (n = 413), children completed three-alternative forced choice matching tasks with faces and bicycles. All tests showed good psychometric properties. The face and bicycle tests were well-matched for difficulty and showed a similar developmental trajectory. Neither the memory nor matching tests were suitable to detect impairments in the youngest groups of children, but both tests appear suitable to screen for face recognition problems in middle childhood. In the current sample, 1.2-5.2% of children showed difficulties with face recognition; 1.2-4% showed face-specific difficulties – that is, poor face recognition with typical object recognition abilities. This is somewhat higher than previous adult estimates: it is possible that face matching tests overestimate the prevalence of face recognition difficulties in children; alternatively, some children may “outgrow” face recognition difficulties

Towards Activity Context using Software Sensors

Service-Oriented Computing delivers the promise of configuring and reconfiguring software systems to address user's needs in a dynamic way. Context-aware computing promises to capture the user's needs and hence the requirements they have on systems. The marriage of both can deliver ad-hoc software solutions relevant to the user in the most current fashion. However, here it is a key to gather information on the users' activity (that is what they are doing). Traditionally any context sensing was conducted with hardware sensors. However, software can also play the same role and in some situations will be more useful to sense the activity of the user. Furthermore they can make use of the fact that Service-oriented systems exchange information through standard protocols. In this paper we discuss our proposed approach to sense the activity of the user making use of software

Trans-Arctic asymmetries, melting pots and weak species cohesion in the low-dispersal amphiboreal seaweed Fucus distichus

Amphiboreal taxa are often composed of vicariant phylogroups and species complexes whose divergence and phylogeographic affinities reflect a shared history of chronic isolation and episodic trans-Arctic dispersal. Ecological filters and shifting selective pressures may also promote selective sweeps, niche shifts and ecological speciation during colonization, but these are seldom considered at biogeographical scales. Here we integrate genetic data and Ecologic Niche Models (ENMs) to investigate the historical biogeography and cohesion of the polymorphic rockweed Fucus distichus throughout its immense amphiboreal range, focusing on trans-Arctic asymmetries, glacial/interglacial dynamics, and integrity of sympatric eco-morphotypes. Populations were sampled throughout the Pacific and the Atlantic, from southern rear-edges to the high-Arctic. They were genotyped for seven microsatellites and an mtDNA spacer, and genetic diversity and structure were assessed from global to local scales. ENMs were used to compare niche divergence and magnitude of post-glacial range shifts in Pacific versus Atlantic sub-ranges. Haplotypic and genotypic data revealed distinct and seemingly isolated Pacific vs Arctic/Atlantic gene-pools, with finer-scale regional sub-structuring pervasive in the Pacific. MtDNA diversity was highly structured and overwhelmingly concentrated in the Pacific. Regionally, Alaska showed the highest intra-population diversity but the lowest levels of endemism. Some sympatric/parapatric ecotypes exhibited distinct genotypic/ haplotypic compositions. Strikingly, niche models revealed higher Pacific tolerance to maximum temperatures and predicted a much more consolidated presence in the NE Atlantic. Glacial and modern ranges overlapped extensively in the Pacific, whereas the modern Atlantic range was largely glaciated or emerged during the Last Glacial Maximum. Higher genetic and ecogeographic diversity supports a primary Pacific diversification and secondary Atlantic colonization, also likely reflecting the much larger and more stable climatic refugia in the Pacific. The relic distribution and reduced ecological/morphological plasticity in the NE Atlantic are hypothesized to reflect functional trans-Arctic bottlenecks, recent colonization or competition with congeners. Within the Pacific, Alaska showed signatures of a post-glacial melting pot of eastern and southern populations. Genetic/ecotypic variation was generally not sufficiently discontinuous or consistent to justify recognizing multiple taxonomic entities, but support a separate species in the eastern Pacific, at the southern rear-edge. We predict that layered patterns of phylogeographic structure, incipient speciation and niche differences might be common among widespread low-dispersal amphiboreal taxa

A Statistically Rigorous Test for the Identification of Parent−Fragment Pairs in LC-MS Datasets

Untargeted global metabolic profiling by liquid chromato-graphy−mass spectrometry generates numerous signals that are due to unknown compounds and whose identification forms an important challenge. The analysis of metabolite fragmentation patterns, following collision-induced dissociation, provides a valuable tool for identification, but can be severely impeded by close chromatographic coelution of distinct metabolites. We propose a new algorithm for identifying related parent−fragment pairs and for distinguishing these from signals due to unrelated compounds. Unlike existing methods, our approach addresses the problem by means of a hypothesis test that is based on the distribution of the recorded ion counts, and thereby provides a statistically rigorous measure of the uncertainty involved in the classification problem. Because of technological constraints, the test is of primary use at low and intermediate ion counts, above which detector saturation causes substantial bias to the recorded ion count. The validity of the test is demonstrated through its application to pairs of coeluting isotopologues and to known parent−fragment pairs, which results in test statistics consistent with the null distribution. The performance of the test is compared with a commonly used Pearson correlation approach and found to be considerably better (e.g., false positive rate of 6.25%, compared with a value of 50% for the correlation for perfectly coeluting ions). Because the algorithm may be used for the analysis of high-mass compounds in addition to metabolic data, we expect it to facilitate the analysis of fragmentation patterns for a wide range of analytical problems

Simplivariate Models: Ideas and First Examples

One of the new expanding areas in functional genomics is metabolomics: measuring the metabolome of an organism. Data being generated in metabolomics studies are very diverse in nature depending on the design underlying the experiment. Traditionally, variation in measurements is conceptually broken down in systematic variation and noise where the latter contains, e.g. technical variation. There is increasing evidence that this distinction does not hold (or is too simple) for metabolomics data. A more useful distinction is in terms of informative and non-informative variation where informative relates to the problem being studied. In most common methods for analyzing metabolomics (or any other high-dimensional x-omics) data this distinction is ignored thereby severely hampering the results of the analysis. This leads to poorly interpretable models and may even obscure the relevant biological information. We developed a framework from first data analysis principles by explicitly formulating the problem of analyzing metabolomics data in terms of informative and non-informative parts. This framework allows for flexible interactions with the biologists involved in formulating prior knowledge of underlying structures. The basic idea is that the informative parts of the complex metabolomics data are approximated by simple components with a biological meaning, e.g. in terms of metabolic pathways or their regulation. Hence, we termed the framework ‘simplivariate models’ which constitutes a new way of looking at metabolomics data. The framework is given in its full generality and exemplified with two methods, IDR analysis and plaid modeling, that fit into the framework. Using this strategy of ‘divide and conquer’, we show that meaningful simplivariate models can be obtained using a real-life microbial metabolomics data set. For instance, one of the simple components contained all the measured intermediates of the Krebs cycle of E. coli. Moreover, these simplivariate models were able to uncover regulatory mechanisms present in the phenylalanine biosynthesis route of E. coli

The Significance of Hair for Face Recognition

Hair is a feature of the head that frequently changes in different situations. For this reason much research in the area of face perception has employed stimuli without hair. To investigate the effect of the presence of hair we used faces with and without hair in a recognition task. Participants took part in trials in which the state of the hair either remained consistent (Same) or switched between learning and test (Switch). It was found that in the Same trials performance did not differ for stimuli presented with and without hair. This implies that there is sufficient information in the internal features of the face for optimal performance in this task. It was also found that performance in the Switch trials was substantially lower than in the Same trials. This drop in accuracy when the stimuli were switched suggests that faces are represented in a holistic manner and that manipulation of the hair causes disruption to this, with implications for the interpretation of some previous studies