Attentional Tracking of Multiple Vehicles in a Highway Driving Scenario

In this paper we introduce a \u27vehicle tracking\u27 task, which tests the ability of a driver to track the location of multiple vehicles on the roadway. Based on the \u27multiple object tracking\u27 task (Pylyshyn & Storm, 1988), the vehicle tracking task presents the driver with an array of identical vehicles immediately in front of the subject vehicle. The task consists of three distinct stages: encoding, during which the target vehicles are indicated to the driver; tracking, during which all vehicles change lanes in a random order; and report, during which the participant indicates the final location of the target vehicles. Using this methodology, we test the accuracy with which university-aged drivers can track multiple vehicles in a 3-lane highway driving scenario. Our particular interest in this paper is how the ability to attend to multiple vehicles changes as task load increases

A Novel and Fast Approach for Population Structure Inference Using Kernel-PCA and Optimization (PSIKO)

Population structure is a confounding factor in Genome Wide Association Studies, increasing the rate of false positive associations. In order to correct for it, several model-based algorithms such as ADMIXTURE and STRUCTURE have been proposed. These tend to suffer from the fact that they have a considerable computational burden, limiting their applicability when used with large datasets, such as those produced by Next Generation Sequencing (NGS) techniques. To address this, non-model based approaches such as SNMF and EIGENSTRAT have been proposed, which scale better with larger data. Here we present a novel non-model based approach, PSIKO, which is based on a unique combination of linear kernel-PCA and least-squares optimization and allows for the inference of admixture coefficients, principal components, and number of founder populations of a dataset. PSIKO has been compared against existing leading methods on a variety of simulation scenarios, as well as on real biological data. We found that in addition to producing results of the same quality as other tested methods, PSIKO scales extremely well with dataset size, being considerably (up to 30 times) faster for longer sequences than even state of the art methods such as SNMF. PSIKO and accompanying manual are freely available at https://www.uea.ac.uk/computing/psiko

The Effects of Task Load and Vehicle Heterogeneity on Performance in the Multiple-Vehicle Tracking Task

When crossing traffic at busy intersections, drivers must keep track of the changing positions of cyclists, pedestrians and other vehicles to avoid collision. Multiple-object tracking is the ability to monitor the positions of a number of selected moving objects (targets) among others (distractors) in a complex scene. Most young adults can track 3-5 items at once but older adults cannot track as many, a finding that may partially explain older drivers’ increased risk at intersections. Because tracking represents an important component of driving, a variant of the multiple-object tracking task called multiple-vehicle was created to measure tracking performance in a driving simulator. However, it is unclear whether tracking while driving works the same as tracking carried out on its own. Laboratory studies suggest that tracking improves when the moving items are heterogeneous, and on the road, it is far more typical that vehicles differ from one another rather than being all the same. Drivers were given the task of tracking the positions of 4 vehicles in a field of 8 on a highway, and the effects of task load (tracking alone, tracking while driving) on tracking performance were measured as a function of whether the target and distractor vehicles were homogeneous. Steering and headway maintenance variability were also assessed. The results indicated that heterogeneity only enabled better tracking when drivers were tracking in isolation. Heterogeneity had no significant effect on tracking when participants were tracking while driving though it did significantly reduce their steering variability

Auxin-Induced Modulation of ETTIN Activity Orchestrates Gene Expression in Arabidopsis

The phytohormone auxin governs crucial developmental decisions throughout the plant life cycle. Auxin signaling is effectuated by auxin response factors (ARFs) whose activity is repressed by Aux/IAA proteins under low auxin levels, but relieved from repression when cellular auxin concentrations increase. ARF3/ETTIN (ETT) is a conserved noncanonical Arabidopsis thaliana ARF that adopts an alternative auxin-sensing mode of translating auxin levels into multiple transcriptional outcomes. However, a mechanistic model for how this auxin-dependent modulation of ETT activity regulates gene expression has not yet been elucidated. Here, we take a genome-wide approach to show how ETT controls developmental processes in the Arabidopsis shoot through its auxin-sensing property. Moreover, analysis of direct ETT targets suggests that ETT functions as a central node in coordinating auxin dynamics and plant development and reveals tight feedback regulation at both the transcriptional and protein-interaction levels. Finally, we present an example to demonstrate how auxin sensitivity of ETT-protein interactions can shape the composition of downstream transcriptomes to ensure specific developmental outcomes. These results show that direct effects of auxin on protein factors, such as ETT-TF complexes, comprise an important part of auxin biology and likely contribute to the vast number of biological processes affected by this simple molecule

Manipulating Drive Characteristics to Study the Effects of Mental Load on Older and Younger Drivers

A driving simulator was used to assess performance in younger and older drivers (M ages 18 and 71 years). The impacts of three challenges were assessed: visibility (clear day, fog), traffic density (low, high) and wayfinding (no challenge, drivers challenged to use signs and landmarks to find their destination). Performance was measured in terms of hazard RT, collisions, wayfinding errors (missed or extra turns), and driving speed. The challenge manipulations produced interactive effects and age was a factor in some of these interactions. Older drivers missed more turns in wayfinding but overall they performed as well or better than younger drivers and reduced their speed more to driving challenges

Combining SNP discovery from next-generation sequencing data with bulked segregant analysis (BSA) to fine-map genes in polyploid wheat

<p>Abstract</p> <p>Background</p> <p>Next generation sequencing (NGS) technologies are providing new ways to accelerate fine-mapping and gene isolation in many species. To date, the majority of these efforts have focused on diploid organisms with readily available whole genome sequence information. In this study, as a proof of concept, we tested the use of NGS for SNP discovery in tetraploid wheat lines differing for the previously cloned grain protein content (GPC) gene <it>GPC-B1</it>. Bulked segregant analysis (BSA) was used to define a subset of putative SNPs within the candidate gene region, which were then used to fine-map <it>GPC-B1</it>.</p> <p>Results</p> <p>We used Illumina paired end technology to sequence mRNA (RNAseq) from near isogenic lines differing across a ~30-cM interval including the <it>GPC-B1 </it>locus. After discriminating for SNPs between the two homoeologous wheat genomes and additional quality filtering, we identified inter-varietal SNPs in wheat unigenes between the parental lines. The relative frequency of these SNPs was examined by RNAseq in two bulked samples made up of homozygous recombinant lines differing for their GPC phenotype. SNPs that were enriched at least 3-fold in the corresponding pool (6.5% of all SNPs) were further evaluated. Marker assays were designed for a subset of the enriched SNPs and mapped using DNA from individuals of each bulk. Thirty nine new SNP markers, corresponding to 67% of the validated SNPs, mapped across a 12.2-cM interval including <it>GPC-B1</it>. This translated to 1 SNP marker per 0.31 cM defining the <it>GPC-B1 </it>gene to within 13-18 genes in syntenic cereal genomes and to a 0.4 cM interval in wheat.</p> <p>Conclusions</p> <p>This study exemplifies the use of RNAseq for SNP discovery in polyploid species and supports the use of BSA as an effective way to target SNPs to specific genetic intervals to fine-map genes in unsequenced genomes.</p

The transcriptome of Euglena gracilis reveals unexpected metabolic capabilities for carbohydrate and natural product biochemistry

Euglena gracilis is a highly complex alga belonging to the green plant line that shows characteristics of both plants and animals, while in evolutionary terms it is most closely related to the protozoan parasites Trypanosoma and Leishmania. This well-studied organism has long been known as a rich source of vitamins A, C and E, as well as amino acids that are essential for the human diet. Here we present de novo transcriptome sequencing and preliminary analysis, providing a basis for the molecular and functional genomics studies that will be required to direct metabolic engineering efforts aimed at enhancing the quality and quantity of high value products from E. gracilis. The transcriptome contains over 30?000 protein-encoding genes, supporting metabolic pathways for lipids, amino acids, carbohydrates and vitamins, along with capabilities for polyketide and non-ribosomal peptide biosynthesis. The metabolic and environmental robustness of Euglena is supported by a substantial capacity for responding to biotic and abiotic stress: it has the capacity to deploy three separate pathways for vitamin C (ascorbate) production, as well as producing vitamin E (?-tocopherol) and, in addition to glutathione, the redox-active thiols nor-trypanothione and ovothiol

A newly-developed community microarray resource for transcriptome profiling in Brassica species enables the confirmation of Brassica-specific expressed sequences

<p>Abstract</p> <p>Background</p> <p>The <it>Brassica </it>species include an important group of crops and provide opportunities for studying the evolutionary consequences of polyploidy. They are related to <it>Arabidopsis thaliana</it>, for which the first complete plant genome sequence was obtained and their genomes show extensive, although imperfect, conserved synteny with that of <it>A. thaliana</it>. A large number of EST sequences, derived from a range of different <it>Brassica </it>species, are available in the public database, but no public microarray resource has so far been developed for these species.</p> <p>Results</p> <p>We assembled unigenes using ~800,000 EST sequences, mainly from three species: <it>B. napus</it>, <it>B. rapa </it>and <it>B. oleracea</it>. The assembly was conducted with the aim of co-assembling ESTs of orthologous genes (including homoeologous pairs of genes in <it>B. napus </it>from each of the A and C genomes), but resolving assemblies of paralogous, or paleo-homoeologous, genes (<it>i.e</it>. the genes related by the ancestral genome triplication observed in diploid <it>Brassica </it>species). 90,864 unique sequence assemblies were developed. These were incorporated into the BAC sequence annotation for the <it>Brassica rapa </it>Genome Sequencing Project, enabling the identification of cognate genomic sequences for a proportion of them. A 60-mer oligo microarray comprising 94,558 probes was developed using the unigene sequences. Gene expression was analysed in reciprocal resynthesised <it>B. napus </it>lines and the <it>B. oleracea </it>and <it>B. rapa </it>lines used to produce them. The analysis showed that significant expression could consistently be detected in leaf tissue for 35,386 unigenes. Expression was detected across all four genotypes for 27,355 unigenes, genome-specific expression patterns were observed for 7,851 unigenes and 180 unigenes displayed other classes of expression pattern. Principal component analysis (PCA) clearly resolved the individual microarray datasets for <it>B. rapa</it>, <it>B. oleracea </it>and resynthesised <it>B. napus</it>. Quantitative differences in expression were observed between the resynthesised <it>B. napus </it>lines for 98 unigenes, most of which could be classified into non-additive expression patterns, including 17 that showed cytoplasm-specific patterns. We further characterized the unigenes for which A genome-specific expression was observed and cognate genomic sequences could be identified. Ten of these unigenes were found to be <it>Brassica</it>-specific sequences, including two that originate from complex loci comprising gene clusters.</p> <p>Conclusion</p> <p>We succeeded in developing a <it>Brassica </it>community microarray resource. Although expression can be measured for the majority of unigenes across species, there were numerous probes that reported in a genome-specific manner. We anticipate that some proportion of these will represent species-specific transcripts and the remainder will be the consequence of variation of sequences within the regions represented by the array probes. Our studies demonstrated that the datasets obtained from the arrays can be used for typical analyses, including PCA and the analysis of differential expression. We have also demonstrated that <it>Brassica</it>-specific transcripts identified <it>in silico </it>in the sequence assembly of public EST database accessions are indeed reported by the array. These would not be detectable using arrays designed using <it>A. thaliana </it>sequences.</p

Computational Tools for Brassica–Arabidopsis Comparative Genomics

Recent advances, such as the availability of extensive genome survey sequence (GSS) data and draft physical maps, are radically transforming the means by which we can dissect Brassica genome structure and systematically relate it to the Arabidopsis model. Hitherto, our view of the co-linearities between these closely related genomes had been largely inferred from comparative RFLP data, necessitating substantial interpolation and expert interpretation. Sequencing of the Brassica rapa genome by the Multinational Brassica Genome Project will, however, enable an entirely computational approach to this problem. Meanwhile we have been developing databases and bioinformatics tools to support our work in Brassica comparative genomics, including a recently completed draft physical map of B. rapa integrated with anchor probes derived from the Arabidopsis genome sequence. We are also exploring new ways to display the emerging Brassica–Arabidopsis sequence homology data. We have mapped all publicly available Brassica sequences in silico to the Arabidopsis TIGR v5 genome sequence and published this in the ATIDB database that uses Generic Genome Browser (GBrowse). This in silico approach potentially identifies all paralogous sequences and so we colour-code the significance of the mappings and offer an integrated, real-time multiple alignment tool to partition them into paralogous groups. The MySQL database driving GBrowse can also be directly interrogated, using the powerful API offered by the Perl Bio∷DB∷GFF methods, facilitating a wide range of data-mining possibilities